Systems and methods for identifying phrases in digital content that are trending

ABSTRACT

The present disclosure is directed to systems and methods to identify trending or temporally popular phrases based on aggregating multiple users&#39; interactions with an aggregate of content. A server may receive identification of a plurality of clicks of encoded uniform resource locator (URL) links. The server may identify, for each of the plurality of clicks, in content identified from decoding the encoded URL links, a plurality of phrases that correspond to a predetermined set of keywords. The server may determine a velocity of clicks on content corresponding to each phrase of the plurality of phrases and identify a trend in one or more phrases of the plurality of phrases based on the velocity of clicks.

FIELD OF THE INVENTION

The present application is generally directed to systems and methods fortracking and scoring user interactions with content. In particular, thepresent application is directed to identifying trending or temporallypopular phrases based on aggregating multiple users' interactions withan aggregate of content

BACKGROUND

With the proliferation of mobile devices and mobile applicationsconnected to the Internet and with the growth of social networking andcontent on the Internet in general, more and more people are sharingcontent with other people on a regular basis. To share content thatpeople may like or that they think others may like, people may forwardlinks to the content to other people via email, tweeting, texting orposting to a web-site or social networking site. Likewise, those peoplemay in turn forward the forwarded content to other people, and so on. Asa result of one person sharing content, a multitude of users mayinteract with the same content. This sharing and forwarding may occurwith a plurality of different content from different sources. It ischallenging to identify, track and analyze the forwarding and sharing ofsuch content via the multitude of users, devices and sources of content.

SUMMARY OF THE INVENTION

The present solution provides multiple techniques for identifying,tracking and analyzing user's interactions with content that may beshared across the Internet. Embodiments of the present solutionidentifies and tracks user's interactions with content, such as viaclicking on shortened URLs or links that may be shared among users. Onetechnique of the present solution provides a relevance score of adigital resource based on user's interactions with digital resources.Another technique of the present solution provides relevance searchresults based on user interaction with content. Another technique of thepresent solution indentifies phrases that are trending or are temporallypopular based on aggregating multiple users' interactions with anaggregate of content. Yet another technique of the present solutionidentifies trending phrases that are relevant to a unique user. Onetechnique of the present solution provides for tracking influence of auser, based on engagement or clicks driven by that user on contentshared by that user, such as via shortened links. Another technique ofthe present solution provides a recommended set of URLs given an inputURL based on user interactions with a list of related URLs.

In one aspect, the present application is directed to a method forrelevance scoring of a digital resource keyword based on user actionsassociated with a plurality of digital resources. The method scorescontent, URLs, domains, phrases or any entity (all of which are examplesof digital resources) based on an expected relevance to an individualuser which may be based on that user's previous engagement with digitalresources. The method may include receiving, by a server, identificationof a first plurality of actions of a user. Each of the first pluralityof actions may include a click by the user on a link associated with adigital resource of a plurality of digital resources. The server mayreceive identification of a second plurality of actions of the user toshare one or more digital resources of the plurality of digitalresources. The server may identify a plurality of keywords from contentof one or more digital resources of the plurality of digital resources.The server may classify patterns from the first plurality of actions,the second plurality of actions and the plurality of keywords. Theserver may generate, based on the pattern classification, a relevancescore responsive to receiving a user identifier and a digital resourcekeyword.

In some embodiments, the server may receive identification of the firstplurality of actions of the user comprising clicks by the user on anencoded link associated with the digital resource. The encoded link mayprovide a shortened version of the link to the digital resource. Theserver may receive identification of the first plurality of actions ofthe user from one or more cookies associated with the user. The servermay receive identification of the second plurality of actions of theuser comprising forwarding at least a portion of content of the digitalresource to a second user. The server may receive identification of thesecond plurality of actions of the user comprising sharing at least aportion of content from the digital resource in a social networkingsite. The server may identify the plurality of keywords from the contentbased on one or more of the following: text, phrases and meta-data. Theserver may identify the plurality of keywords from analyzing text orphrases in media files, such as video and/or audio files.

In some embodiments, the server may classify patterns from the pluralityof digital resources. The server may match the digital resource keywordto the pattern classification data of the user identified by the useridentifier. The server may receive the user identifier and the digitalresource keyword comprising any of the following: uniform resourcelocator, domain name and a phrase. The server may generate a data scoreidentifying an amount or quality of data classified for the user. Thedigital resource may include any of the following: content, a web page,a uniform resource locator, a domain name and a phrase.

In another aspect, the present application is directed to a system forrelevance scoring of a digital resource keyword based on user actionsassociated with a plurality of digital resources. The system scorescontent, URLs, domains, phrases or any entity (all of which are examplesof digital resources) based on an expected relevance to an individualuser which may be based on that user's previous engagement with digitalresources. The system may include a server receiving identification of afirst plurality of actions associated with digital resources and asecond plurality actions of user engagement with digital resources suchas via viewing of the digital resource by others to whom the user sharedthe digital resource. Each of the first plurality of actions may includea click by the user on a link associated with a digital resource of aplurality of digital resources. Each of the second plurality of actionsof the user may include an action to share one or more digital resourcesof the plurality of digital resources. A content extractor may identifya plurality of keywords from content of one or more digital resources ofthe plurality of digital resources. A classifier may classify patternsfrom the first plurality of actions, the second plurality of actions andthe plurality of keywords. In addition, a relevance score generator maygenerate, responsive to the classifier, a relevance score responsive toreceiving a user identifier and a digital resource keyword.

In some embodiments, the first plurality of actions of the user includesclicks by the user on an encoded link associated with the digitalresource. The encoded link may provide a shortened version of the linkto the digital resource. The server may identify the first plurality ofactions of the user from one or more cookies associated with the user.The second plurality of actions of the user may comprise forwarding atleast a portion of content of the digital resource to a second user. Thesecond plurality of actions of the user may include sharing at least aportion of content from the digital resource in a social networkingsite.

In certain embodiments, the content extractor may identify the pluralityof keywords from the content based on one or more of the following:text, phrases, images and meta-data. The content extractor may identifythe plurality of keywords from media by analyzing text, phrases orcontent of media files, such as video and/or audio files. The relevancescore generator may match, via the classifier, the digital resourcekeyword to the pattern classification data of the user identified by theuser identifier. The classifier may further classify patterns from theplurality of digital resources. The relevance score generator mayreceive the digital resource keyword comprising any of the following:uniform resource locator, domain name and a phrase. The relevance scoregenerator may generate a data score identifying an amount of data of theclassifier for the user. The digital resource may, in some embodiments,comprise any of the following: content, a web page, a uniform resourcelocator, a domain name and a phrase.

In yet another aspect, the present application is directed to a methodfor providing search results based on user interaction with content. Themethod may include receiving, by a server, identification of a pluralityof clicks of encoded uniform resource locator (URL) links. The servermay identify, for each of the plurality of clicks, data about a user whoclicked an encoded URL link and traffic data associated with a devicefrom which the user clicked the encoded URL link. The server may store arecord for each click of the plurality of clicks, the record comprisingdata about the user and traffic data associated with each click. Theserver may determine, based on the records, a relevancy score for eachcontent identified from decoding the encoded URL links. The server maycommunicate, responsive to receiving a request to search content basedon a keyword, a set of search results based on the keyword and therelevancy score. The search results may be based on audience segmentingparameters identified via the request, such as geography, language orother demographic parameters.

In some embodiments, the server decodes each of the encoded URL links.The server may identify data about the user from a cookie communicatedvia a click by the user on the encoded URL link. The server may identifytraffic data comprising one or more of a browser type, a referring website, a source internet protocol address and a destination internetprotocol address. In certain embodiments, the server may determine anengagement score for each content based on a number of clicks receivedvia one or more encoded URL links to the content. Each of the number ofclicks may be weighted based on when received. The server may determinea distribution score for each content based on a number of clicks fromdifferent sources via one or more encoded URL links to the content. Theserver may determine a social score for each content. The server maydetermine a frequency normalization value for each content by extractingkeywords from the content, normalizing the keywords and storing thekeywords and corresponding normalization values into a database.

In certain embodiments, the server may apply a time decay function tothe relevancy score based on the length of time a content has beenstored in a record after being identified from decoding the encoded URLlinks. The server may determine the relevancy score by a combination oftwo or more of a social score, a distribution score, an engagementscore, a frequency normalization value and a time decay function. Insome embodiments, the server may order the set of search results byrelevance score. Although sometimes referred to as scores, these scoremay be considered weights to be applied to determine the relevancescore.

In still another aspect, the present application is directed to a systemfor providing search results based on user interaction with content. Aserver may receive identification of a plurality of clicks of encodeduniform resource locator (URL) links. A click tracker may identify, foreach of the plurality of clicks, data about a user who clicked anencoded URL link and traffic data associated with a device from whichthe user clicked the encoded URL link. A database may store a record foreach click of the plurality of clicks. The record may include data aboutthe user and/or traffic data associated with each click. A relevancyscorer may determine, based on the records, a relevancy score for eachcontent identified from decoding the encoded URL links. The server maycommunicate a set of search results based on the keyword and therelevancy score responsive to receiving a request to search contentbased on a keyword. The server may perform the search based on anyaudience segmentation parameters, such as a geography, language or otherdemographics.

In some embodiments, the server may decode each of the encoded URLlinks. The click tracker may identify data about the user from a cookiecommunicated via a click by the user on the encoded URL link. The clicktracker may identify traffic data comprising one or more of a browsertype, a referring web site, a source internet protocol address and adestination internet protocol address. In certain embodiments, therelevancy scorer determines a distribution score for each content basedon a number of clicks from different sources via one or more encoded URLlinks to the content. The relevancy scorer may determine an engagementscore for each content based on a number of clicks received via one ormore encoded URL links to the content. Each of the number of clicks maybe weighted based on when received.

In some embodiments, the relevancy scorer determines a frequencynormalization value for each content by extracting keywords from thecontent. The relevancy scorer may normalize the keywords and may storethe keywords and corresponding normalization values into a database. Therelevancy scorer may apply a time decay function to the relevancy scorebased on the length of time a content has been stored in a record afterbeing identified from decoding the encoded URL links. The relevancyscorer may determine the relevancy score by a combination of two or moreof a distribution score, an engagement score, a frequency normalizationvalue and a time decay function. The relevancy scorer generate and/ormay order search results by relevance score.

In another aspect, the present application is directed to a method foridentifying trends in phrases based on users interaction with contentcontaining, related to or associated with the phrases. The method mayidentify trending or temporally popular phrases based on aggregatingmultiple users' interactions with an aggregate of content. The methodmay include receiving, by a server, identification of a plurality ofclicks of encoded uniform resource locator (URL) links. The server mayidentify, for each of the plurality of clicks, in content identifiedfrom decoding the encoded URL links, a plurality of phrases thatcorrespond to a predetermined set of keywords. The server may determinea velocity of clicks on content corresponding to each phrase of theplurality of phrases. The server may identify a trend in one or morephrases of the plurality of phrases based on the velocity of clicks. Theserver may receive identification of the plurality of clicks from aplurality of different users via a plurality of different sources.

In some embodiments, the server decodes each of the encoded URL links toobtain a URL to the content. The server may identify the plurality ofphrases in the content based on one of text or meta-data in the content.The server may identify one or more phrases of the plurality of phrasesin the content that deviates from a predetermined norm for the content.The server may determine velocity based on a number of clicks via one ormore encoded URL links within a predetermined time period on contentcorresponding to a phrase. The server may determine velocity based on arate of clicks via one or more encoded URL links to contentcorresponding to a phrase. The server may determine velocity based on achange in rate of clicks via one or more encoded URL links to contentcorresponding to a phrase. In some embodiments, the server enumerates alist of phrases from the plurality of phrases based on increasingvelocity of clicks. The server may enumerate a list of phrases from theplurality of phrases based on decreasing velocity of clicks.

In yet another aspect, the present application is directed to a systemfor identifying trends in phrases based on users interaction withcontent containing, related to or associated with the phrases. Thesystem may identify trending or temporally popular phrases based onaggregating multiple users' interactions with an aggregate of content.The system may include a server receiving identification of a pluralityof clicks of encoded uniform resource locator (URL) links. A contentextractor may identify, for each of the plurality of clicks, in contentidentified from decoding the encoded URL links, a plurality of phrasesthat correspond to a predetermined set of keywords. A trending enginemay determine a velocity of clicks on content corresponding to eachphrase of the plurality of phrases and identifying a trend in one ormore phrases of the plurality of phrases based on the velocity ofclicks.

In some embodiments, the server receives identification of the pluralityof clicks from a plurality of different users via a plurality ofdifferent sources. The server may decode each of the encoded URL linksto obtain a URL to the content. In certain embodiments, the contentextractor identifies the plurality of phrases in the content based onone of text or meta-data in the content. In some embodiments, thecontent extractor identifies a plurality of phrases in the media contentbased on analyzing text or meta-data in a media file, such as videoand/or audio files. The content extractor may identify one or phrases ofthe plurality of phrases in the content that deviates from apredetermined norm for the content. The trending engine may determinevelocity based on a number of clicks via one or more encoded URL linkswithin a predetermined time period on content corresponding to a phrase.The trending engine may determine velocity based on a rate of clicks viaone or more encoded URL links to content corresponding to a phrase. Thetrending engine may determine velocity based on a change in rate ofclicks via one or more encoded URL links to content corresponding to aphrase. The trending engine may enumerate a list of phrases from theplurality of phrases based on increasing velocity of clicks. Thetrending engine may enumerate a list of phrases from the plurality ofphrases based on decreasing velocity of clicks.

In yet another aspect, the present application is directed to a methodfor identifying which phrases are trending across an aggregate of usersthat are relevant to a specific user. The method may include receiving,by a server, identification of a user. The server may identify aplurality of phrases that are trending upwards based on velocity ofclicks to content containing, related to or associated with theplurality of phrases. The server may identify trending or temporallypopular phrases based on aggregating multiple users' interactions withan aggregate of content. The server may determine a relevance score foreach phrase of the plurality of phrases that are trending upwards basedon identification of the user and actions of the user on contentassociated with each phrase, such as user clicking on contentidentifying or related to each phrase. The server may identify one ormore phrases of the plurality of phrases based on relevance score.

In some embodiments, the server may receive identification of the uservia a cookie. The server may identify the plurality of phrases that aretrending upwards above a predetermined threshold. The server mayidentify an enumerated list of the plurality of phrases based ontrending from highest to lowest. The server may determine the relevancescore for each phrases based on a plurality of actions of the user toclick on a link to content user on content related to or identifyingeach phrase. The server may determine the relevance score for eachphrase based on a plurality of actions of the user to share contentassociated with each phrase. The server may select the one or morephrases with the highest relevance score. In some embodiments, theserver selects the one or more phrases with a relevance score greaterthan a predetermined threshold. The server may select the one or morephrases with a relevance score greater than a first predeterminedthreshold and that are trending within a second predetermined threshold.The server may select content to serve the user based on the identifiedone or more phrases.

In yet another aspect, the present application is directed to a systemfor identifying phrases trending across an aggregate of userinteractions that are relevant to a specific user. The system mayinclude a server receiving identification of a user. A trending enginemay identify a plurality of phrases that are trending upwards based onvelocity of clicks from a plurality of user to content containing,associated with or related to the plurality of phrases. The trendingengine may identify trending or temporally popular phrases based onaggregating multiple users' interactions with an aggregate of content. Arelevance scorer may determine for each phrase of the plurality ofphrases that are trending upwards based on identification of the userand actions of the user on content associated with or identifying eachphrase, such as a user clicking on content related to each phrase. Theserver may identify one or more phrases of the plurality of phrasesbased on relevance score.

In some embodiments, the server receives identification of the user viaa cookie. The trending engine may identify the plurality of phrases thatare trending upwards above a predetermined threshold. The trendingengine may identify the plurality of phrases that are trending based onrank ordered relative to other phrases. The trending engine may identifyan enumerated list of the plurality of phrases based on trending fromhighest to lowest. In certain embodiments, the relevance scorerdetermines the relevance score for each phrase based on a plurality ofactions of the user to click on a link to content relating to oridentifying each phrase. The relevance scorer may determine therelevance score for each phrase for each user based on a plurality ofactions of the user to share content associated with each phrase. Theserver may select the identified one or more phrases with the highestrelevance score. The server may select the identified one or morephrases with a relevance score greater than a predetermined threshold.The server may select the identified one or more phrases with arelevance score greater than a first predetermined threshold and thatare trending within a second predetermined threshold. In someembodiments, one of the server or a second server selects content toserve the user based on the identified one or more phrases.

In yet another aspect, the present application is directed to a methodfor tracking influence of a user on content shared via encoded uniformresource locator (URL) links. Measuring influence of a user may identifywhat level of engagement the user drives to content when the user sharescontent with other users, such as via encoded links. A high influencermay be a user who drives a high level of engagement with content whenthe user shares content. A low influencer may be a user who does notdrive a high level of engagement, or otherwise drives a low level ofengagement with content when the user shares content. The method mayinclude receiving, by a server, identification of a user for each of aplurality of encoded uniform resource locator (URL) links. The servermay identify a plurality of keywords from content identified by eachencoded URL link. The server may determine a number of actions via aplurality of users that decoded each encoded URL link of the pluralityof encoded URL links of the user. The server may store, in a profile ofthe user, information on the one or more keywords and the number ofactions.

In some embodiments, the server receives one or more requests from theuser to encode one or more the plurality of encoded URL links. Theserver may identify the user via one of a cookie or a user account withthe server. The server may identify the plurality of keywords from thecontent identified by the decoded URL links based on one or more of thefollowing: text, phrases and meta-data. The server may identify theplurality of keywords from media associated with or in the contentidentified by the decoded URL links based on analyzing text or phrasesin the media file, such as video and/or audio files. The server maydetermine the plurality of actions of the plurality of users on theencoded URLs links from one or more cookies associated with each of theplurality of users. The server may receive identification of theplurality of actions of the plurality of users comprising forwarding byeach of the plurality of the users an encoded URL link. The server mayreceive identification of the plurality of actions of the plurality ofusers comprising sharing by each of the plurality of the users anencoded URL link in one or more social networking sites. In certainembodiments, the server may receive a request for a relevance score forthe user and a keyword. The server may generate the relevance scoreresponsive to the request and one of up weighting or down weighting therelevance score based on the profile of the user. The server maygenerate the relevance score responsive to the request and generatingthe relevance score based on the profile of the user and the profiles ofusers who decoded the encoded URL links of the user.

In still another aspect, the present application is directed to a systemfor tracking influence of a user on content shared via encoded uniformresource locator (URL) links. The system may include a server receivingidentification of a user for each of a plurality of encoded uniformresource locator (URL) links. A content extractor may identify aplurality of keywords from content identified by each encoded URL link.A click tracker determining a number of actions via a plurality of usersthat decoded each encoded URL link of the plurality of encoded URL linksof the user. The server may store in a profile of the user informationon the one or more keywords and the number of actions.

In certain embodiments, the server may receive one or more requests fromthe user to encode one or more of the plurality of encoded URL links.The server may identify the user via one of a cookie or a user accountwith the server. The server may identify the plurality of keywords fromthe content identified by the decoded URL links based on one or more ofthe following: text, phrases and meta-data. The server may identify theplurality of keywords from media associated with or in the contentidentified by the decoded URL links based on analyzing text or phrasesin the media file, such as video and/or audio files. The click trackermay determine the plurality of actions of the plurality of users on theencoded URLs links from one or more cookies associated with each of theplurality of users.

In some embodiments, the plurality of actions of the plurality of usersmay include forwarding by each of the plurality of the users an encodedURL link. The plurality of actions of the plurality of users may includesharing by each of the plurality of the users an encoded URL link in oneor more social networking sites. The server may receive a request for arelevance score for the user and a keyword. A relevance scorer maygenerate the relevance score responsive to the request and one of upweights or down weights the relevance score based on the profile of theuser. A relevance scorer may generate, responsive to the request, therelevance score based on the profile of the user and the profiles ofusers who decoded the encoded URL links of the user.

In still another aspect, the present application is directed to a methodfor providing a recommended list of uniform resource locators (URLs)responsive to a uniform resource locator (URL). The method may includeidentifying, by a server, a plurality of users that clicked on anencoded uniform resource locator (URL) link corresponding to a URL. Theserver may identify a plurality of encoded URL links clicked by each ofthe plurality of users. The server may determine a number of users whoclicked on each encoded URL link of the plurality of encoded URL linksand also clicked on the encoded URL link. The server may enumerate,responsive a request comprising the URL, a list of URLs and theircorresponding score based on the determination, each URL of the list ofURLs corresponding to one of the plurality of encoded URL links.

In certain embodiments, the server may receive identification of a clickof the encoded URL link from each of the plurality of users. The servermay determine a decoded URL corresponding to the encoded URL link. Theserver may identify the user via a cookie. The server may track clickson encoded URL links for each user of the plurality of users. The servermay generate a click co-occurrence map that correlates the plurality ofusers that clicked on the encoded URL link. The server may generate theclick co-occurrence map or co-occurrence map that correlates theplurality of users that clicked on the encoded URL link to the pluralityof encoded URLs link that each of the plurality of users has clicked.The server may communicate a response to the request. The response mayinclude the enumerated list of URLs and their corresponding score. Theserver may enumerate the list of URLS ordered by the number of users whoclicked on the encoded URL link corresponding to the URL and clicked onthe encoded URL link. The server may filter the list of URLs based oncontent or domain.

In another aspect, the present application is directed to a system forproviding a recommended list of uniform resource locators (URLs)responsive to a uniform resource locator (URL). The system may include aserver identifying a plurality of users that clicked on an encodeduniform resource locator (URL) link corresponding to a URL. A clicktracker may identify a plurality of encoded URL links clicked by each ofthe plurality of users. A correlation engine may determine a number ofusers who clicked on each encoded URL link of the plurality of encodedURL links and also clicked on the encoded URL link. The server may,responsive to a request comprising the URL, enumerate a list of URLs.Each URL of the list of URLs may correspond to one of the plurality ofencoded URL links.

In certain embodiments, the server may receive identification of a clickof the encoded URL link from each of the plurality of users. The servermay determine a decoded URL corresponding to the encoded URL link. Incertain embodiments, the server identifies the user via a cookie. Theclick tracker may track clicks on encoded URL links for each user of theplurality of users. The correlation engine may generate a clickcoherency or co-occurrence map that correlates the plurality of usersthat clicked on the encoded URL link. The correlation engine maygenerate the click co-occurrence map that correlates the plurality ofusers that clicked on the encoded URL link to the plurality of encodedURLs link that each of the plurality of users has clicked. In certainembodiments, the server communicates a response to the request, theresponse comprising the enumerated list of URLs and their correspondingscore. The server may enumerate the list of URLS ordered by the numberof users who clicked on the encoded URL link corresponding to the URLand clicked on the encoded URL link. The server may filter the list ofURLs based on content identified by the URL.

The details of various embodiments of the invention are set forth in theaccompanying drawings and the description below.

BRIEF DESCRIPTION OF DRAWINGS

The foregoing and other objects, aspects, features, and advantages ofthe present invention will become more apparent and better understood byreferring to the following description taken in conjunction with theaccompanying drawings, in which:

FIG. 1A is a block diagram of an embodiment of a network environment fora client to access servers;

FIGS. 1B and 1C are block diagrams of embodiments of a computing device;

FIG. 2 is a diagram of an embodiment of a system to shorten, share andtrack links;

FIG. 3A is a block diagram of an embodiment of a system for relevancescoring of a digital resource;

FIG. 3B is a flow diagram of an embodiment of a method for relevancescoring of a digital resource;

FIG. 4A is a block diagram of an embodiment of a system for providingsearch results based on user interaction with content;

FIG. 4B is a flow diagram of an embodiment of a method for providingsearch results based on user interaction with content;

FIG. 5A is a block diagram of an embodiment of a system for identifyingtrends in phrases of content;

FIG. 5B is a flow diagram of an embodiment of a method for identifyingtrends in phrases of content;

FIG. 6A is a block diagram of an embodiment of a system for identifyingtrending and relevance of phrases;

FIG. 6B is a flow diagram of an embodiment of a method for identifyingtrending and relevance of phrases;

FIG. 7A is a block diagram of an embodiment of a system for trackinginfluence of a user on content shared via encoded uniform resourcelocator (URL) links;

FIG. 7B is a flow diagram of an embodiment of a method for trackinginfluence of a user on content shared via encoded uniform resourcelocator (URL) links;

FIG. 8A is a block diagram of an embodiment of a system for providing arecommended list of uniform resource locators (URLs) responsive to auniform resource locator (URL); and

FIG. 8B is a flow diagram of an embodiment of a method for providing arecommended list of uniform resource locators (URLs) responsive to auniform resource locator (URL).

In the drawings, like reference numbers generally indicate identical,functionally similar, and/or structurally similar elements.

DETAILED DESCRIPTION

For purposes of reading the description of the various embodimentsbelow, the following enumeration of the sections of the specificationand their respective contents may be helpful:

-   -   Section A describes a network and computing environment which        may be useful for practicing embodiments described herein;    -   Section B describes embodiments of a system to shorten, track        and analyze links;    -   Section C describes embodiments of systems and methods for        relevance scoring of a digital resource;    -   Section D describes embodiments of systems and methods for        providing search results based on user interaction with content;    -   Section E describes embodiments of systems and methods for        identifying trends in phrases of content;    -   Section F describes embodiments of systems and methods for        identifying trending and relevance of phrases for a user;    -   Section G describes embodiments of systems and methods for        tracking influence of a user on content shared via encoded        uniform resource locator (URL) link; and    -   Section H describes embodiments of systems and methods for        providing recommended list of uniform resource locators (URLs)        responsive to a uniform resource locator (URL).        A. Network and Computing Environment

Prior to discussing the specifics of embodiments of the systems andmethods of server and/or client, it is helpful to discuss the networkand computing environments in which such embodiments may be deployed.Referring to FIG. 1A, an embodiment of a network environment isdepicted. In brief overview, the network environment includes one ormore clients 102 a-102 n (also generally referred to as local machine(s)102, client(s) 102, client node(s) 102, client machine(s) 102, clientcomputer(s) 102, client device(s) 102, endpoint(s) 102, or endpointnode(s) 102) in communication with one or more servers 106 a-106 n (alsogenerally referred to as server(s) 106, node 106, or remote machine(s)106) via one or more networks 104. In some embodiments, a client 102 hasthe capacity to function as both a client node seeking access toresources provided by a server and as a server providing access tohosted resources for other clients 102 a-102 n.

Although FIG. 1A shows a network 104 between the clients 102 and theservers 106, the clients 102 and the servers 106 may be on the samenetwork 104. The network 104 can be a local-area network (LAN), such asa company Intranet, a metropolitan area network (MAN), or a wide areanetwork (WAN), such as the Internet or the World Wide Web. In someembodiments, there are multiple networks 104 between the clients 102 andthe servers 106. In one of these embodiments, a network 104′ (not shown)may be a private network and a network 104 may be a public network. Inanother of these embodiments, a network 104 may be a private network anda network 104′ a public network. In still another of these embodiments,networks 104 and 104′ may both be private networks.

The network 104 may be any type and/or form of network and may includeany of the following: a point-to-point network, a broadcast network, awide area network, a local area network, a telecommunications network, adata communication network, a computer network, an ATM (AsynchronousTransfer Mode) network, a SONET (Synchronous Optical Network) network, aSDH (Synchronous Digital Hierarchy) network, a wireless network and awireline network. In some embodiments, the network 104 may comprise awireless link, such as an infrared channel or satellite band. Thetopology of the network 104 may be a bus, star, or ring networktopology. The network 104 may be of any such network topology as knownto those ordinarily skilled in the art capable of supporting theoperations described herein. The network may comprise mobile telephonenetworks utilizing any protocol or protocols used to communicate amongmobile devices, including AMPS, TDMA, CDMA, GSM, GPRS or UMTS. In someembodiments, different types of data may be transmitted via differentprotocols. In other embodiments, the same types of data may betransmitted via different protocols.

In some embodiments, the system may include multiple, logically-groupedservers 106. In one of these embodiments, the logical group of serversmay be referred to as a server farm 38 or a machine farm 38. In anotherof these embodiments, the servers 106 may be geographically dispersed.In other embodiments, a machine farm 38 may be administered as a singleentity. In still other embodiments, the machine farm 38 includes aplurality of machine farms 38. The servers 106 within each machine farm38 can be heterogeneous—one or more of the servers 106 or machines 106can operate according to one type of operating system platform (e.g.,WINDOWS NT, manufactured by Microsoft Corp. of Redmond, Wash.), whileone or more of the other servers 106 can operate on according to anothertype of operating system platform (e.g., Unix or Linux).

In one embodiment, servers 106 in the machine farm 38 may be stored inhigh-density rack systems, along with associated storage systems, andlocated in an enterprise data center. In this embodiment, consolidatingthe servers 106 in this way may improve system manageability, datasecurity, the physical security of the system, and system performance bylocating servers 106 and high performance storage systems on localizedhigh performance networks. Centralizing the servers 106 and storagesystems and coupling them with advanced system management tools allowsmore efficient use of server resources.

The servers 106 of each machine farm 38 do not need to be physicallyproximate to another server 106 in the same machine farm 38. Thus, thegroup of servers 106 logically grouped as a machine farm 38 may beinterconnected using a wide-area network (WAN) connection or ametropolitan-area network (MAN) connection. For example, a machine farm38 may include servers 106 physically located in different continents ordifferent regions of a continent, country, state, city, campus, or room.Data transmission speeds between servers 106 in the machine farm 38 canbe increased if the servers 106 are connected using a local-area network(LAN) connection or some form of direct connection. Additionally, aheterogeneous machine farm 38 may include one or more servers 106operating according to a type of operating system, while one or moreother servers 106 execute one or more types of hypervisors rather thanoperating systems. In these embodiments, hypervisors may be used toemulate virtual hardware, partition physical hardware, virtualizephysical hardware, and execute virtual machines that provide access tocomputing environments. Hypervisors may include those manufactured byVMWare, Inc., of Palo Alto, Calif.; the Xen hypervisor, an open sourceproduct whose development is overseen by Citrix Systems, Inc.; theVirtualServer or virtual PC hypervisors provided by Microsoft or others.

Management of the machine farm 38 may be de-centralized. For example,one or more servers 106 may comprise components, subsystems and modulesto support one or more management services for the machine farm 38. Inone of these embodiments, one or more servers 106 provide functionalityfor management of dynamic data, including techniques for handlingfailover, data replication, and increasing the robustness of the machinefarm 38. Each server 106 may communicate with a persistent store and, insome embodiments, with a dynamic store.

Server 106 may be a file server, application server, web server, proxyserver, appliance, network appliance, gateway, gateway, gateway server,virtualization server, deployment server, SSL VPN server, or firewall.In one embodiment, the server 106 may be referred to as a remote machineor a node. In another embodiment, a plurality of nodes 290 may be in thepath between any two communicating servers.

The client 102 and server 106 may be deployed as and/or executed as anytype and form of computing device, such as a computer, network device orappliance capable of communicating on any type and form of network andperforming the operations described herein. FIGS. 1B and 1C depict blockdiagrams of a computing device 100 useful for practicing an embodimentof the client 102 or a server 106. As shown in FIGS. 1B and 1C, eachcomputing device 100 includes a central processing unit 121, and a mainmemory unit 122. As shown in FIG. 1B, a computing device 100 may includea storage device 128, an installation device 116, a network interface118, an I/O controller 123, display devices 124 a-102 n, a keyboard 126and a pointing device 127, such as a mouse. The storage device 128 mayinclude, without limitation, an operating system, software, and softwarefor sharing, tracking and analyzing links 120. As shown in FIG. 1C, eachcomputing device 100 may also include additional optional elements, suchas a memory port 103, a bridge 170, one or more input/output devices 130a-130 n (generally referred to using reference numeral 130), and a cachememory 140 in communication with the central processing unit 121.

The central processing unit 121 is any logic circuitry that responds toand processes instructions fetched from the main memory unit 122. Inmany embodiments, the central processing unit 121 is provided by amicroprocessor unit, such as: those manufactured by Intel Corporation ofMountain View, Calif.; those manufactured by Motorola Corporation ofSchaumburg, Ill.; those manufactured by Transmeta Corporation of SantaClara, Calif.; the RS/6000 processor, those manufactured byInternational Business Machines of White Plains, N.Y.; or thosemanufactured by Advanced Micro Devices of Sunnyvale, Calif. Thecomputing device 100 may be based on any of these processors, or anyother processor capable of operating as described herein.

Main memory unit 122 may be one or more memory chips capable of storingdata and allowing any storage location to be directly accessed by themicroprocessor 121, such as Static random access memory (SRAM), BurstSRAM or SynchBurst SRAM (BSRAM), Dynamic random access memory (DRAM),Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM (EDRAM), Extended DataOutput RAM (EDO RAM), Extended Data Output DRAM (EDO DRAM), BurstExtended Data Output DRAM (BEDO DRAM), Enhanced DRAM (EDRAM),synchronous DRAM (SDRAM), JEDEC SRAM, PC 100 SDRAM, Double Data RateSDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), SyncLink DRAM (SLDRAM),Direct Rambus DRAM (DRDRAM), or Ferroelectric RAM (FRAM). The mainmemory 122 may be based on any of the above described memory chips, orany other available memory chips capable of operating as describedherein. In the embodiment shown in FIG. 1B, the processor 121communicates with main memory 122 via a system bus 150 (described inmore detail below). FIG. 1C depicts an embodiment of a computing device100 in which the processor communicates directly with main memory 122via a memory port 103. For example, in FIG. 1C the main memory 122 maybe DRDRAM.

FIG. 1C depicts an embodiment in which the main processor 121communicates directly with cache memory 140 via a secondary bus,sometimes referred to as a backside bus. In other embodiments, the mainprocessor 121 communicates with cache memory 140 using the system bus150. Cache memory 140 typically has a faster response time than mainmemory 122 and is typically provided by SRAM, BSRAM, or EDRAM. In theembodiment shown in FIG. 1C, the processor 121 communicates with variousI/O devices 130 via a local system bus 150. Various buses may be used toconnect the central processing unit 121 to any of the I/O devices 130,including a VESA VL bus, an ISA bus, an EISA bus, a MicroChannelArchitecture (MCA) bus, a PCI bus, a PCI-X bus, a PCI-Express bus, or aNuBus. For embodiments in which the I/O device is a video display 124,the processor 121 may use an Advanced Graphics Port (AGP) to communicatewith the display 124. FIG. 1C depicts an embodiment of a computer 100 inwhich the main processor 121 communicates directly with I/O device 130 bvia HYPERTRANSPORT, RAPIDIO, or INFINIBAND communications technology.FIG. 1C also depicts an embodiment in which local busses and directcommunication are mixed: the processor 121 communicates with I/O device130 a using a local interconnect bus while communicating with I/O device130 b directly.

A wide variety of I/O devices 130 a-130 n may be present in thecomputing device 100. Input devices include keyboards, mice, trackpads,trackballs, microphones, dials, and drawing tablets. Output devicesinclude video displays, speakers, inkjet printers, laser printers, anddye-sublimation printers. The I/O devices may be controlled by an I/Ocontroller 123 as shown in FIG. 1B. The I/O controller may control oneor more I/O devices such as a keyboard 126 and a pointing device 127,e.g., a mouse or optical pen. Furthermore, an I/O device may alsoprovide storage and/or an installation medium 116 for the computingdevice 100. In still other embodiments, the computing device 100 mayprovide USB connections (not shown) to receive handheld USB storagedevices such as the USB Flash Drive line of devices manufactured byTwintech Industry, Inc. of Los Alamitos, Calif.

Referring again to FIG. 1B, the computing device 100 may support anysuitable installation device 116, such as a floppy disk drive forreceiving floppy disks such as 3.5-inch, 5.25-inch disks or ZIP disks, aCD-ROM drive, a CD-R/RW drive, a DVD-ROM drive, a flash memory drive,tape drives of various formats, USB device, hard-drive or any otherdevice suitable for installing software and programs. The computingdevice 100 may further comprise a storage device, such as one or morehard disk drives or redundant arrays of independent disks, for storingan operating system and other related software, and for storingapplication software programs such as any program related to thesoftware 120 for sharing, tracking and analyzing links. Optionally, anyof the installation devices 116 could also be used as the storagedevice. Additionally, the operating system and the software can be runfrom a bootable medium, for example, a bootable CD, such as KNOPPIX, abootable CD for GNU/Linux that is available as a GNU/Linux distributionfrom knoppix.net.

Furthermore, the computing device 100 may include a network interface118 to interface to the network 104 through a variety of connectionsincluding, but not limited to, standard telephone lines, LAN or WANlinks (e.g., 802.11, T1, T3, 56 kb, X.25, SNA, DECNET), broadbandconnections (e.g., ISDN, Frame Relay, ATM, Gigabit Ethernet,Ethernet-over-SONET), wireless connections, or some combination of anyor all of the above. Connections can be established using a variety ofcommunication protocols (e.g., TCP/IP, IPX, SPX, NetBIOS, Ethernet,ARCNET, SONET, SDH, Fiber Distributed Data Interface (FDDI), RS232, IEEE802.11, IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, CDMA, GSM, WiMax anddirect asynchronous connections). In one embodiment, the computingdevice 100 communicates with other computing devices 100′ via any typeand/or form of gateway or tunneling protocol such as Secure Socket Layer(SSL) or Transport Layer Security (TLS). The network interface 118 maycomprise a built-in network adapter, network interface card, PCMCIAnetwork card, card bus network adapter, wireless network adapter, USBnetwork adapter, modem or any other device suitable for interfacing thecomputing device 100 to any type of network capable of communication andperforming the operations described herein.

In some embodiments, the computing device 100 may comprise or beconnected to multiple display devices 124 a-124 n, which each may be ofthe same or different type and/or form. As such, any of the I/O devices130 a-130 n and/or the I/O controller 123 may comprise any type and/orform of suitable hardware, software, or combination of hardware andsoftware to support, enable or provide for the connection and use ofmultiple display devices 124 a-124 n by the computing device 100. Forexample, the computing device 100 may include any type and/or form ofvideo adapter, video card, driver, and/or library to interface,communicate, connect or otherwise use the display devices 124 a-124 n.In one embodiment, a video adapter may comprise multiple connectors tointerface to multiple display devices 124 a-124 n. In other embodiments,the computing device 100 may include multiple video adapters, with eachvideo adapter connected to one or more of the display devices 124 a-124n. In some embodiments, any portion of the operating system of thecomputing device 100 may be configured for using multiple displays 124a-124 n. In other embodiments, one or more of the display devices 124a-124 n may be provided by one or more other computing devices, such ascomputing devices 100 a and 100 b connected to the computing device 100,for example, via a network. These embodiments may include any type ofsoftware designed and constructed to use another computer's displaydevice as a second display device 124 a for the computing device 100.One ordinarily skilled in the art will recognize and appreciate thevarious ways and embodiments that a computing device 100 may beconfigured to have multiple display devices 124 a-124 n.

In further embodiments, an I/O device 130 may be a bridge between thesystem bus 150 and an external communication bus, such as a USB bus, anApple Desktop Bus, an RS-232 serial connection, a SCSI bus, a FireWirebus, a FireWire 800 bus, an Ethernet bus, an AppleTalk bus, a GigabitEthernet bus, an Asynchronous Transfer Mode bus, a HIPPI bus, a SuperHIPPI bus, a SerialPlus bus, a SCI/LAMP bus, a FibreChannel bus, aSerial Attached small computer system interface bus, or a HDMI bus.

A computing device 100 of the sort depicted in FIGS. 1B and 1C typicallyoperates under the control of operating systems, which controlscheduling of tasks and access to system resources. The computing device100 can be running any operating system such as any of the versions ofthe MICROSOFT WINDOWS operating systems, the different releases of theUnix and Linux operating systems, any version of the MAC OS forMacintosh computers, any embedded operating system, any real-timeoperating system, any open source operating system, any proprietaryoperating system, any operating systems for mobile computing devices, orany other operating system capable of running on the computing deviceand performing the operations described herein. Typical operatingsystems include, but are not limited to: WINDOWS 3.x, WINDOWS 95,WINDOWS 98, WINDOWS 2000, WINDOWS NT 3.51, WINDOWS NT 4.0, WINDOWS CE,WINDOWS MOBILE, WINDOWS XP, WINDOWS VISTA, and WINDOWS 7, all of whichare manufactured by Microsoft Corporation of Redmond, Wash.; MAC OS,manufactured by Apple Computer of Cupertino, Calif.; OS/2, manufacturedby International Business Machines of Armonk, N.Y.; and Linux, afreely-available operating system distributed by Caldera Corp. of SaltLake City, Utah, or any type and/or form of a Unix operating system,among others.

The computer system 100 can be any workstation, telephone, desktopcomputer, laptop or notebook computer, server, handheld computer, mobiletelephone or other portable telecommunications device, media playingdevice, a gaming system, mobile computing device, or any other typeand/or form of computing, telecommunications or media device that iscapable of communication. The computer system 100 has sufficientprocessor power and memory capacity to perform the operations describedherein. For example, the computer system 100 may comprise a device ofthe IPOD, IPHONE, or APPLE TV family of devices manufactured by AppleComputer of Cupertino, Calif., a PLAYSTATION 2, PLAYSTATION 3, orPERSONAL PLAYSTATION PORTABLE (PSP) device manufactured by the SonyCorporation of Tokyo, Japan, a NINTENDO DS, NINTENDO GAMEBOY, NINTENDOGAMEBOY ADVANCED, NINTENDO REVOLUTION, or a NINTENDO WII devicemanufactured by Nintendo Co., Ltd., of Kyoto, Japan, an XBOX or XBOX 360device manufactured by the Microsoft Corporation of Redmond, Wash.

In some embodiments, the computing device 100 may have differentprocessors, operating systems, and input devices consistent with thedevice. For example, in one embodiment, the computing device 100 is aTREO 180, 270, 600, 650, 680, 700p, 700w, or 750 smart phonemanufactured by Palm, Inc. In some of these embodiments, the TREO smartphone is operated under the control of the PalmOS operating system andincludes a stylus input device as well as a five-way navigator device.

In other embodiments the computing device 100 is a mobile device, suchas a JAVA-enabled cellular telephone or personal digital assistant(PDA), such as the i55sr, i58sr, i85s, i88s, i90c, i95c1, or the im1100,all of which are manufactured by Motorola Corp. of Schaumburg, Ill., the6035 or the 7135, manufactured by Kyocera of Kyoto, Japan, or the i300or i330, manufactured by Samsung Electronics Co., Ltd., of Seoul, Korea.In some embodiments, the computing device 100 is a mobile devicemanufactured by Nokia of Finland, or by Sony Ericsson MobileCommunications AB of Lund, Sweden.

In still other embodiments, the computing device 100 is a Blackberryhandheld or smart phone, such as the devices manufactured by Research InMotion Limited, including the Blackberry 7100 series, 8700 series, 7700series, 7200 series, the Blackberry 7520, or the Blackberry Pearl 8100.In yet other embodiments, the computing device 100 is a smart phone,Pocket PC, Pocket PC Phone, or other handheld mobile device supportingMicrosoft Windows Mobile Software. Moreover, the computing device 100can be any workstation, desktop computer, laptop or notebook computer,server, handheld computer, mobile telephone, any other computer, orother form of computing or telecommunications device that is capable ofcommunication and that has sufficient processor power and memorycapacity to perform the operations described herein.

In some embodiments, the computing device 100 is a digital audio player.In one of these embodiments, the computing device 100 is a digital audioplayer such as the Apple IPOD, IPOD Touch, and IPOD NANO lines ofdevices, manufactured by Apple Computer of Cupertino, Calif. In anotherof these embodiments, the digital audio player may function as both aportable media player and as a mass storage device. In otherembodiments, the computing device 100 is a digital audio player such asthe DigitalAudimpression opportunity layer Select MP3 players,manufactured by Samsung Electronics America, of Ridgefield Park, N.J.,or the Motorola m500 or m25 Digital Audio Players, manufactured byMotorola Inc. of Schaumburg, Ill. In still other embodiments, thecomputing device 100 is a portable media player, such as the Zen VisionW, the Zen Vision series, the Zen Portable Media Center devices, or theDigital MP3 line of MP3 players, manufactured by Creative TechnologiesLtd. In yet other embodiments, the computing device 100 is a portablemedia player or digital audio player supporting file formats including,but not limited to, MP3, WAV, M4A/AAC, WMA Protected AAC, AIFF, Audibleaudiobook, Apple Lossless audio file formats and .mov, .m4v, and.mp4MPEG-4 (H.264/MPEG-4 AVC) video file formats.

In some embodiments, the communications device 102 includes acombination of devices, such as a mobile phone combined with a digitalaudio player or portable media player. In one of these embodiments, thecommunications device 102 is a smartphone, for example, an iPhonemanufactured by Apple Computer, or a Blackberry device, manufactured byResearch In Motion Limited. In yet another embodiment, thecommunications device 102 is a laptop or desktop computer equipped witha web browser and a microphone and speaker system, such as a telephonyheadset. In these embodiments, the communications devices 102 areweb-enabled and can receive and initiate phone calls. In otherembodiments, the communications device 102 is a Motorola RAZR orMotorola ROKR line of combination digital audio players and mobilephones.

In some embodiments, the status of one or more machines 102, 106 in thenetwork 104 is monitored, generally as part of network management. Inone of these embodiments, the status of a machine may include anidentification of load information (e.g., the number of processes on themachine, CPU and memory utilization), of port information (e.g., thenumber of available communication ports and the port addresses), or ofsession status (e.g., the duration and type of processes, and whether aprocess is active or idle). In another of these embodiments, thisinformation may be identified by a plurality of metrics, and theplurality of metrics can be applied at least in part towards decisionsin load distribution, network traffic management, and network failurerecovery as well as any aspects of operations of the present solutiondescribed herein. Aspects of the operating environments and componentsdescribed above will become apparent in the context of the systems andmethods disclosed herein.

B. System for Shortening, Sharing and Tracking Links

Referring now to FIG. 2, embodiments of a system 120 for shortening,sharing and tracking links is depicted. In brief overview, a linkingsystem 120 executes on one or more server(s) 106A-N and may be accessedby a plurality of clients 102A-102N via a network 104. The linkingsystem 120 may include a link encoder 210 that shortens a link, such asa uniform resource locator (URL) 205 to a resource on a destinationserver 106. The link encoder may encode (e.g., shorten) the linkresponsive to a request to shorten the URL 205. The client may include alink system API or application 225A-225N to interface with the linkingsystem 120 and request to shorten the link. The request may include acookie 255 identifying user and client information. The link encoder 210may generate or otherwise provide an encoded URL 211 to a client. Thelink encoder may store in a database 230 information about the encodingof the URL and the URL 205. The user tracker 215 may track informationabout the user, such as via the cookie 255 and store the information inthe database 230.

Via the browser of the client, a user may click on or otherwise activate250 the encoded URL 211 which directs the browser to the linking system120. The click action 250 may be a request to decode the URL. The clickaction or request thereof may include a cookie 255′ which provides userand client information. The link decoder 212 may decode the encoded URL211, such as via database 230. For example, the link decoder may performa lookup of the URL corresponding to the shortened or encoded URL. Thelinking system, such as via decoder 212, may send a redirect, such as anHypertext Transfer Protocol (HTTP) redirect (e.g., 301 redirect), to theclient to the decoded URL 205. The browser of the client may access orbe directed to the URL 205 of the link destination server 106. The clicktracker 220 may track user actions on the encoded URL, such as when theencoded URL was clicked, from what source and by what user and storesuch tracking in the database 230. The click tracker and/or user trackermay track user information from the cookies 255′ in connection with orassociated with the click action 250. The click tracker and/or usertracker may track and store user the referrer information from therequest.

A user of client 102A may share via email, web-site posting, socialnetworking, etc. the encoded URL 211 to any one or more other users,such as users of clients 102B-102N. Any of these users may click on oractivate the encoded URL 211. The plurality of click actions on theencoded URL provide a stream of requests from user click actions todecode the encoded URL, which may be generally referred to as a clickstream 250′. The linking system via link decoder 212 may decode theencoded URL and redirect each of the clients to the URL 205. The usertracker and click tracker may track information on the user and theclick actions of the client stream 250′ in the database. The clickanalyzer 235 may provide metrics on the click actions of the encodedURL, such as the number of clicks, the times of clicks and the sourcesof the clicks.

In further details, the linking system 120 may comprise an application,program, library, process, service, script, task or any type and form ofexecutable instructions executable or executing on a device. The linkingsystem may operate on a plurality of servers 106A-106N. The linkingsystem may comprise logic, function, and operations for shortening,sharing and tracking links, such as URLs. The linking system maycomprise application programming interfaces, such as web services, XML,Jason (JSON), etc. for accessing the functionality, operations and/ordata of the linking system. The linking system may include one or moremodules, components or executables for providing these APIs andperforming the function and operations described herein. For example, insome embodiments, the linking system may include a link encoder 210, alink decoder 212, a user tracker 215, a click tracker 220 and a clickanalyzer 235. The modules, components or executables of the linkingsystem may operate in a client/server architecture. The modules,components or executables of the linking system may operate in adistributed manner across multiple devices.

The linking system may include, operate, communicate or interface with alinking system API or application 225A-225N (generally referred to as225). In some embodiments, an application 225 may execute on the clientthat communicates with or interfaces to the linking system to encode anddecode URLs. In some embodiments, an application 225 may include anyportion of the linking system. In some embodiments, the application maybe a mobile application, generally referred to as an app, executing on amobile device, such as a smart phone or tablet device. In someembodiments, the application may include an add-on, extension, script,ActiveX control, applet, widget or other types and forms of executableinstructions executed by or in a browser. In some embodiments, theapplication may include, use or call one or more APIs to the linkingsystem. The application may be programmed to programmatically integratethe linking system, or interface thereto, into the application. Via theone or more APIs, the application may access data from the linkingsystem. Via the one or more APIs, the application may perform or executeany of the functions or operations of the linking system. Via the one ormore APIs, the application may perform or execute any of the systems andmethods described herein.

The link encoder may include an application, program, library, process,service, script, task or any type and form of executable instructionsfor encoding a link. The link encoder may shorten a URL. The encoded URLmay be referred to or be a shortened URL. Creating a shortened link maybe referred to as encoding. The link encoder may shorten the URL to apredetermined string length or to a predetermined number of characters.The link encoder may shorten the URL to a length determined responsiveto the length of the URL to be encoded. The link encoder may encode theURL into an encoded URL using an encoding scheme. In some embodiments,the link encoder applies a hash to the URL to generate or produce theencoded URL. In some embodiments, the encoded URL is a hash or hashcode. In some embodiments, the link encoder transforms the URL using atransformation function, such as a reversible transformation function.In some embodiments, the link encoder removes a portion of the URL. Insome embodiments, the link encoder rewrites a portion of the URL with aportion of another URL. In some embodiments, the link encoder encryptsthe URL or a portion of the URL using one or more encryption keys. Insome embodiments, the link encoder generates a unique identifier for theencoded URL in which the unique identifier uniquely identifies the URL.In some embodiments, the link encoder obfuscates information from theoriginal URL, such as information relating to a directory structure ofthe server from the URL. The link encoder may encode the URL into anencoded URL that comprises a domain name hosted by or recognized by thelinking system or any server thereof. The link encoder may encode theURL into an encoded URL that comprises a domain name configured,specified or identified by a user, such as a domain name of an entitythat is a user of the linking system. The link encoder may encode theURL to identify a URL of the linking system, such as a landing page orintermediate page of the linking system. In some embodiments, the linkencoder may encode the URL to be resolved to an intermediate URL or pageof the linking system prior to being redirected by the linking system tothe URL after decoding.

The link decoder may include an application, program, library, process,service, script, task or any type and form of executable instructionsfor decoding an encoded link. The link decoder may be designed andconstructed to decode, un-shorten, generate, produce or otherwiseprovide the original URL corresponding to the encoded URL. Clicking on ashortened link may be referred to or called decoding. In someembodiments, the link decoder determines the URL from the encoded URLvia lookup in the database. In some embodiments, the link decoder usesthe encoded URL as an index to look up the URL in the database. In someembodiments, the link decoder uses the encoded URL as a hash index intoa has table of the database. In some embodiments, the link decoder usersthe encoded URL or a portion thereof as a unique identifier to the URLstored in memory, storage of database of the linking system. In someembodiments, the link decoder uses a decoding scheme designed andconstructed to perform the reverse of the encoding scheme or otherwiseproduce or generate the original input (e.g., the URL) to the encodingscheme. In some embodiments, the link decoder applies a reversetransformation function to the encoded URL. In some embodiments, thelink decoder replaces a portion of the encoded URL with a portion of theURL. In some embodiments, the link decoder un-obfuscates information inthe encoded URL to a portion of the original URL. In some embodiments,the link decoder replaces a domain name of the encoded URL with a domainname of the URL.

In some embodiments, the linking system, such as via link decoder,generates, issues or communicates a redirect responsive to receipt of anencoded URL and/or decoding the encoded URL. In some embodiments, thelinking system issues any type of 3XX HTTP redirect. In HTTP, a redirectis a response with a status code beginning with a 3XX that induces abrowser to go to another location. In some embodiments, the response orstatus code includes an annotation describing the reason, which allowsfor the correct subsequent action (such as changing links in the case ofcode 301, a permanent change of address). In some embodiments, thelinking system issues a 301 type of HTTP redirect. In some embodiments,the redirect response comprises or uses a technique for making a webpageavailable under many URLs. In some embodiments, the linking system usesscripting for redirection. In some embodiments, the linking system usesa refresh meta tag or HTTP refresh header technique for redirection.

In some embodiments, when the user clicks on or activates the shortenedlink 211 the user or browser is taken to an interstitial page of thelinking system, and then using an HTTP redirect page, an intermediatewebsite of the linking system refers the user to the final destinationsite of URL 204. While doing so, the intermediate website may track fromwhich website the user clicked on the short link, stores various userspecific data, and notes any related cookies, or if there are nocookies, stores a new cookie on the user for the future.

A user of one device, such as client device 102, may share the encodedURL 211 with a plurality of users, such as users on client devices102B-102N. A user or application may share the encoded URL by emailingthe encoded URL to a user. A user or application may share the encodedURL by posting or publishing the encoded URL to a web-site. A user orapplication may share the encoded URL by posting, publishing orforwarding the encoded URL to a social networking site, such as but notlimited to LinkedIn or Facebook. A user or application may share theencoded URL by texting the encoded URL. A user or application may sharethe encoded URL by posting or communicating the encoded URL via acommunication tool, such as Skype or Instant Messenger. A user orapplication may share the encoded URL by serving the encoded URL incontent served by a web-site. A user or application may share theencoded URL by serving the encoded URL in an advertisement or impressionopportunity served by an ad server. A user or application may share theencoded URL via the linking system API or app 225, such as via a linkingsystem bookmark applet on a browser. Any user receiving the encoded URLfrom any device may click on or activate the encoded URL to communicatewith the linking system and be directed to the URL decoded from orcorresponding to the encoded URL.

A click tracker 220 may include an application, program, library,process, service, script, task or any type and form of executableinstructions for tracking actions regarding an encoded URL and/ordecoding the encoded URL. In some embodiments, the click trackeridentifies each instance of a user clicking on an encoded URL and tracksthe number of clicks for the URL via the encoded URL in the database230. In some embodiments, the click tracker identifies each instance ofa user clicking on any of a plurality of encoded URLs that correspond toa URL and tracks the number of clicks for the URL via any encoded URL inthe database 230. In some embodiments, the click tracker may identifyand track via the database any temporal information regarding the clickson the encoded URL, such as date and time of the click action 250. Insome embodiments, the click tracker may identify and track via thedatabase any source information regarding the clicks on the encoded URL,such a source internet protocol (IP) address, source port and MachineAccess Control (MAC) identifier of the device from which the userclicked on the encoded URL. In some embodiments, the click tracker mayidentify and track via the database any header, field or otherinformation via any application layer payload, such as the HTTP payloadof the packet(s) carrying the click action or request to decode the URL.In some embodiments, the click tracker may identify and track via thedatabase the HTTP header field of referrer to identify and track the URLor webpage from which the click action or request was referred ororiginated.

A user tracker 215 may include an application, program, library,process, service, script, task or any type and form of executableinstructions for tracking and managing information regarding users ofthe linking system and/or users interacting with encoded and decodedURLs. The user tracker may include an interface, such as a web page, tohave users register as users of the linking system. The user tracker maycollect via registration authentication information of the user, such asa user identifier and a password. The user tracker may identify andcollect information from any type and form of cookie 255. The usertracker may receive the cookie via a request to shorten a URL. Thecookie may be any third-party cookie. The cookie may be a cookiegenerated by, provided by or tracked for the linking system. The usertracker or linking system may insert, modify or provide any data,information or attributes in the cookie for the linking system. The usertracker or linking system may include or provide a cookie 255′ incommunicating the redirect response for a click action that decodes theencored URL. The cookie may comprise information, data or attributesthat identify the user, any user's actions, preferences of the userand/or history of user activity or behavior. The cookie may compriseinformation, data or attributes that identify any click actions. Thecookie may comprise information, data or attributes that identify theURL and/or any encoding and/or decoding of the URL. The cookie maycomprise information, data or attributes of redirection or the redirectresponse by the linking system. The user tracker may identify and trackany user activity in encoding URLs. The user tracker may identify andtrack any user activity in decoding URLs. The user tracker may identifyand track any user activity in sharing encoded URLs. The user trackermay store tracked information, data and attributes to the database.

In some embodiments, the click tracker comprises the user tracker or aportion thereof. In some embodiments, the user tracker comprises theclick tracker or a portion thereof. In some embodiments, a tracker 215or 220 comprises both the click tracker and user tracker. In someembodiments, the user tracker is integrated with, interfaced to orcommunicates with the click tracker. The user tracker and click trackermay be designed and constructed to track and store to the databaseinformation about encoding URLs, decoding URLs and clicks of encodedURLS in association with users connected to the encoding of the URLs,decoding of the URLS and clicking on the encoded URLs.

The database 230 may include an application, program, library, process,service, script, task or any type and form of executable instructionsfor tracking and managing information and data stored by, accessed byand/or used by the linking system or any modules or components thereof.The database may be any type and form of Structured Query Language (SQL)database. The database may be any type and form of object oriented orobject based database. The database may be any type and form ofin-memory or real-time memory database. The database may comprise anytype and form of graphical database. The database may comprise any typeand form of data warehousing and/or analytical database. The databasemay comprise any type and form of multi-dimensional database. Thedatabase may store any data and information from any of the functions,operations, systems and methods described herein.

A click analyzer 235 may include an application, program, library,process, service, script, task or any type and form of executableinstructions for analyzing, searching and/or reporting any of theinformation, data and metrics stored by the linking system in thedatabase 230. The click analyzer may include any type and form of onlineanalytical processing (OLAP). The click analyzer may analyze click anduser data stored in the database to determine a number of clicks to aURL per encoding of the URL. The click analyzer may analyze click anduser data stored in the database to determine a number of clicks to aURL for all encodings of the URL across a plurality of users. The clickanalyzer may analyze click and user data stored in the database todetermine a location of users who clicked on an encoded URL, such aswhat countries the clicks originated from. The click analyzer mayanalyze click and user data stored in the database to determine thedifferent referring sites from which users clicked on an encoded URL.The click analyzer may analyze click and user data stored in thedatabase to determine the different types of clients or clientapplications from which users clicked on an encoded URL. The clickanalyzer may analyze click and user data stored in the database todetermine a number of clicks over a predetermined time period or afrequency of clicks. The click analyzer may analyze click and user datastored in the database to determine a number of conversations acrossdifferent social media networks regarding or in connection with anencoded URL. The click analyzer may provide any data, information and/oranalysis in a graphical format, such as any type and form of statisticalcharts or diagrams.

A plurality of users may click on 250 the same encoded URL 211. Each ofthese users may also click on a plurality of different encoded URLs tothe same URL or to different URLs. The plurality of click actions 250may generated and/or providing data that is tracked and stored via thelinking system. The set of data resulting from a click action and/ordata associated with the click and/or collected, tracked, and analyzedeither statically or in real-time by the linking system may be referredto as a clickstream 250′ or click stream 250′. The click stream mayinclude any data tracked by the user tracker. The click stream mayinclude any data tracked by the click tracker, such as any networktraffic data. The click stream may include any data provided by thebrowser. The click stream may include any data provided via the HTTPrequest. The click stream may include any data analyzed by the clickanalyzer. The click stream may include any data traversing the linkingsystem.

C. Systems and Methods for Scoring Digital Resources

Referring now to FIGS. 3A and 3B, systems and methods for scoring adigital resource, such as content is depicted. These systems and methodsscore content, URLs, domains, phrases or any entity (all of which areexamples of digital resources) based on an expected relevance to anindividual user which may be based on that user's previous engagementwith digital resources. Relevance is from the perspective of a user to adigital resource. By tracking what digital resources a user hasinteracted with and analyzing and classifying those digital resources,these systems and methods generate a score that identifies how similar apresented or identified digital resource is to other digital resourcesthe user has engaged with For example, by tracking what content a userhas clicked on, and by analyzing and classifying that content, thesystems and methods of the present solution, when shown a new set ofcontent, may generate a score determining how similar that content is toother content the user has engaged with. The relevance score produced bythese systems and methods provide an indicator of relevance ofidentified digital resource is to a particular user.

Embodiments of the system of the present solution may take as input: (i)a list of actions associated with a given user (user actions) and (ii) alist of actions associated with entities (global actions). For useractions, the system may identify users via a cookie or a set of cookies.For example, a typical user may have actions associated with a set ofcookies, all associated with an individual user. In some embodiments, acookie-classifier can be used to associate multiple cookies with aspecific individual. A list of actions associated with users may beobtained outside of cookies, such as importing web logs or user activitydatabases. Global actions may include sharing a link, or sharing a pieceof content. Typical examples may include a user forwarding a link viaemail to another user (e.g., the link is the entity, the forwarding isthe action). In another example, a global action may include sharing aparagraph of content on a social media platform like a Facebook update(e.g., the update content is the entity, and “shared on Facebook” is theaction). Embodiments of the system of the present solution may use clickstream 250′ data.

In example operation, the system processes the global actions through acontent extractor, which extracts meta-data, phrases, keywords that arespecific to the entity. The system may provide the global actions andextracted content as input to a pattern classifier. The system may alsoprovide user actions to the pattern classifier. The pattern classifiermay classify the global actions, the user actions and the extractedcontent into a plurality of classes that is stored by the system. Thesystem may receive a request to score a digital resource for a specificuser. For example the digital resource may include piece of data, akeyword, phrase, URL, or domain name, and the user may be identified bya user ID such as the user's cookie ID. The system matches the digitalresource and user identifier with the classified data from theclassifier, and the output is a content or relevance score and a datascore. The relevance score indentifies how closely the digital resource(e.g., new piece of data) matches the digital resources associated withthat user. The data score estimates or identified how valid therelevance score is. The data score may be based on how much data wasassociated with the intersection of the user action and the globalactions.

Referring now to FIG. 3A, an embodiment of a system for scoring digitalresources 340 is depicted. In brief overview, embodiments of the linkingsystem 120 may receive or obtain user actions 305 and global actions310, such as via a click stream 250′. The linking system may include acontent extractor 315 to identify keywords, meta-data and phrases fromcontent identified by any of the user actions and/or the global actions.The linking system may include a classifier 320 that classifies patternsfrom the user actions, global actions and the keywords, meta-data and/orphrases from the content extractor. A relevance scorer 325 receives arequest to generate a score for a user id 345 and a digital resourceidentifier 342 that identifies a digital resource 240. The relevancescorer matches the user id and digital resource identifier via theclassification data of the classifier and generates a relevance score330 to identify the relevance of the identified digital resource for theidentified user based on what digital resources the user has previouslyinteracted with. The relevance scorer may generate a data score thatidentifies or estimates the validity or quality of the relevance score.

In further details, a digital resource 340 may comprise any type andform of electronic, digital or web based resource (sometimes referred toas an entity or digital entity). The digital resource may be a domainname. The digital resource may be a web-site. The digital resource maybe a URL. The digital resource may be an encoded URL. The digitalresource may be a web page. The digital resource may be a keyword indigital content. The digital resource may be a phrase in electroniccontent. The digital resource may be meta-data in or of digital content.The digital resource may be digital content. The digital resource may bea file. The digital resource may be a portion, copy or snippet ofdigital content or a file. The digital resource may be an advertisement.The digital resource may be a text or SMS message. The digital resourcemay be an email. The digital resource may be an IM or chat message. Thedigital resource may be an IP based audio and/or video communication.The digital resource may be a posting on a web-site. The digitalresource may be a discussion or conversation, or portion thereof, on aweb-site. The digital resource may be a message, posting or content on asocial networking site. The digital resource may be digital audio, suchas an audio file. The digital resource may be music or music file. Thedigital resource may be a video. The digital resource may be an image.The digital resource may be a graphic file. The digital resource may bean application, program, library, program or script. The digitalresource may be a device.

The digital resource identifier or id 342 may comprise any type and formof identifier associated with, corresponding to or that otherwiseidentifies a digital resource. The digital resource id may be a uniqueidentifier. The digital resource id may be a hash code. The digitalresource id may be a hash of the digital resource. The digital resourceid may be a name of the digital resource. The digital resource id may bea URL of or to the digital resource. The digital resource id may be amemory location of the digital resource. The digital resource id may bea storage location of the digital resource. The digital resource id maybe a name of a file corresponding to the digital resource. The digitalresource id may be the digital resource itself, such as a URLidentifying the digital resource of the URL itself or a domain nameidentifying the digital resource of a domain.

The user actions 305 may comprise any type and form of actions of auser. In some embodiments, the user actions may comprise any actions ofa user to interact or interacting with a digital resource. the useractions may comprise any actions of a user to request, access, obtain,view, print, edit, user or otherwise process a digital resource. In someembodiments, a user action comprises registering and/or logging in tothe linking system. In some embodiments, a user action comprisesregistering and/or logging in to a web-site. In some embodiments, a useraction comprises encoding a URL or requesting the linking system toencode the URL. In some embodiments, a user action comprises clicking oractivating by a user a URL. In some embodiments, a user action comprisesclicking or activating by a user an encoded URL. In some embodiments, auser action comprises requesting a browser to go to a URL or web-page.In some embodiments, a user action comprises a pointer over or mouseover of a keyword, phrase or URL on a web page. In some embodiments, auser action comprises a selection of a keyword, phrase or URL on a webpage. In some embodiments, a user action comprises a traversal betweenURLS or web-links, such as clicking on a hyperlink of one page to get toanother page of a web-site. In some embodiments, a user action compriseslaunching, executing or using a browser, such as a browser of a certaintype. In some embodiments, user actions comprise a history of activityof a user on a computer, browser or web-site, including date and time ofsuch activity. In some embodiments, user actions comprise a log or fileof activity of a user on a computer, browser or web-site, including dateand time of such activity. User actions may comprise the click stream orany portion thereof.

The global actions 310 may include any type and form of actionsregarding sharing, forwarding, propagating or otherwise providing adigital resource to another entity, user or another digital resource. Insome embodiments, a global actions comprises a user electronicallycommunicating a digital resource or digital resource identifier to anentity, user or digital resource. In some embodiments, a global actioncomprises a user emailing a digital resource or digital resourceidentifier. In some embodiments, a global action comprises a usertexting or sending an SMS message comprising a digital resource ordigital resource identifier. In some embodiments, a global actioncomprises a user instant messaging a digital resource or digitalresource identifier. In some embodiments, a global action comprises auser posting a digital resource to a web-site. In some embodiments, aglobal action comprises a user posting, sharing or provide a digitalresource or digital resource identifier via, in or to a socialnetworking site. In some embodiments, a global action comprises a usercutting, copying and/or pasting a digital resource to another digitalresource, such as copying and pasting a portion of content of a web-siteto a social networking web-site. In some embodiments, a global actioncomprises a user forwarding, sharing or providing an encoded URL toanother user, entity or digital resource. In some embodiments, a globalaction comprises a user transforming or processing a digital resource inone form or format to a digital resource in another form or format.Global actions may comprise the click stream or any portion thereof.

The content extractor 315 may comprise an application, program, library,process, service, script, task or any type and form of executableinstructions for identifying, extracting, or processing keywords,phrases and data from content. The content extractor may be designed andconstructed to identify, obtain or retrieve content from a digitalresource, such as a web page identified by a URL. The content extractormay be designed and constructed to identify, determine, and/or extractkeywords and/or phrases associated with, corresponding to from contentof a digital resource. The content extractor may identify, determine,and/or extract keywords and/or phrases corresponding to or matching apredetermined list or enumerations of keywords and/or phrases. Thecontent extractor may identify and retrieve content for a URL decodedfrom an encoded URL. The content extractor may identify and retrievekeywords and/or phrases from content identified by a URL decoded from anencoded URL. The content extractor may identify and retrieve content,and/or keywords and/or phrases, from a predetermined portion of adigital resource. The content extractor may identify and retrieve textareas of a digital resource. The content extractor may identify andretrieve keywords and/or phrases from text areas of the digitalresource. The content extractor may identify and retrieve meta-data fromor about a digital resource. The content extractor may identify andretrieve keywords and/or phrases from the meta-data. In someembodiments, the content extractor may identify and retrieve content,and/or keywords and/or phrases, from user selected or defined portionsof the digital resource. In some embodiments, the content extractor mayidentify a keyword in the digital resource. In some embodiments, thecontent extractor may identify a phrase in the digital resource. Thecontent extractor may identify one or more URLs on a web page. In someembodiments, the content extractor may identify URLs from predeterminedportions of the page. In some embodiments, the content extractor mayidentify URLs from user selected or defined portions of a page. Thecontent extractor may retrieve content from the identified one or moreURLs and identify or retrieve keywords and/or phrases from such content.

In view of any media content, such as video and/or audio files, thecontent extractor may be designed and constructed to analyze the contentof such media to determine any text, phrases or meta-data containedtherein or related thereto. In some embodiments, the content extractormay include any type and form of voice or audio recognition technologyto identify audio content in the media, such as spoken words, music orsounds. In some embodiments, the content extractor may convert any ofthe audio content into corresponding text. In some embodiments, thecontent extractor may identify text or phrases from the text convertedfrom and corresponding to the audio content. In some embodiments, thecontent extractor may include any type and form of video processing andanalysis technology that identifies persons, location, objects or thingsin a video. From such video processing and analysis, the contentextractor may provide a description, such as in text format, of thesubject matter of or the persons, location, objects or things in thevideo. In some embodiments, the content extractor may identify text orphrases from the description determines from and corresponding to thevideo content.

The classifier 320 may comprise an application, program, library,process, service, script, task or any type and form of executableinstructions for performing or providing pattern classification of a setof data. The classifier may use any type and form of classificationscheme or algorithm to identify a sub-population, class or category towhich a new observation or item belongs in which the identity of thesub-population, class or category for the new observation or item is notknown. The classifier may perform pattern recognition, which is theassignment of some sort of output value (or label) to a given inputvalue (or instance), according to some specific algorithm. Via patternrecognition, the classifier attempts to assign each input value to oneof a given set of classes (for example, determine whether a given emailis “spam” or “non-spam”). Classifier may classify based on a training,learning or established set of data containing observations or itemswith a known sub-population, class or category. The classifier maycomprise any type of classifier, such as a neural network, supportvector machines, k-nearest neighbors, Gaussian mixture model, Gaussian,naive Bayes, decision tree and/or Radial Basis Function (RBF)classifier.

The classifier may take as input any one or more of the following: adigital resource, a digital identifier, user actions and global actions.The classifier may take as input any one or more of the keywords and/orphrases identified by the content extractor. The classifier may classifythis input to assign or designate a sub-population, class or category toeach input or sets of input. The classifier may perform thisclassification on a per user basis. The classifier make take inputassociated with a user, such as user actions and global actions for aparticular user, and classify such input into classes or categories andstore such classification in association with the user. The classifiermake take input associated with a user, such as user actions and globalactions for a particular user, identify keywords from the input andclassify the keywords into classes or categories and store suchclassification in association with the user.

The classifier may classify the input into categories or classes basedon keywords and/or phrases identified from the digital resource and/orfrom content of, identified by or associated with the digital resource.The classifier may classify the input into categories or classes basedon subject matter. The classifier may classify the input into categoriesor classes based on topics. The classifier may classify the input intocategories or classes based on context. The classifier may classify theinput into categories or classes based on areas of interest. Theclassifier may classify the input into categories or classes based onpreferences of the user. The classifier may classify the input intocategories or classes based on favorites of the user. The classifier mayclassify the input into categories or classes based on an affinity oraffinities of the user. The classifier may classify the input intocategories or classes based on type of digital resource. The classifiermay classify the input into categories or classes based on source of thedigital resource. The classifier may classify the input into categoriesor classes based on digital resource identifier. The classifier mayclassify the input into categories or classes based on type of action.The classifier may classify the input into categories or classes basedon encoded URLs. The classifier may classify the input into categoriesor classes based on decoding URLs. The classifier may classify the inputinto categories or classes based on URLs. The classifier may classifythe input into categories or classes based on domain names. Theclassifier may classify the input into categories or classes based ontemporal information, such as time and/or date of interaction with thedigital resource.

The classifier may classify the input based on a cross-section, matchingor association of user actions to global actions. The classifier mayidentify those URLs encoded by a user and the encoded URLs clicked on bythe same user but encoded from and shared by other users. The classifiermay match those URLs encoded by a user to those global actions in whichthe user's encoded URLS were shared. The classifier may classify theseURLs into categories or classes. The classifier may classify keywordsand/or phrases from these URLs or content associated therewith intocategories or classes. The classifier may classify into categories orclasses based time and/or date of interaction with encoded URLs.

The relevance scorer 325 may comprise an application, program, library,process, service, script, task or any type and form of executableinstructions for generating and/or providing a score for a digitalresource. The relevance scorer and/or classifier may perform any typeand form of statistical analysis or modeling of the classification data.The relevance scorer may perform any type and form of fuzzy logicmatching to match an input, such as a digital resource identifier, tothe classification data and/or statistical analysis or model to generateor provide a relevance score 330. The relevance scorer may receive orprocess as input a digital resource identifier 342 and a user identifier345. Based on this input, the relevance scorer may determine howrelevant the digital resource identified by the digital resourceidentifier is to the user identified by the user id based on that usersprevious interaction with digital resources as may be represented orreflected in the classification data and/or statistical analysis ormodel.

The relevance scorer may classify the digital resource identified by thedigital resource identifier into an existing class or category of theclassification data and/or statistical analysis or model for the userspecified by the user id. In some embodiments, the relevance scorerdetermines whether the classification of the digital resource identifiedby the digital resource identifier fits into an existing class/categoryor a new class/category of the classification data and/or statisticalanalysis or model for the user specified by the user id. In someembodiments, the relevance scorer determines a number of classes orcategories into which the digital resource identified by the digitalresource identifier may be classified. In some embodiments, therelevance scorer determines a number of other digital resourcespreviously classified in the class or category into which the digitalresource identified by the digital resource identifier may beclassified. In some embodiments, the relevance scorer determinestemporal information (time sand dates of interaction, velocity of rateof interaction, etc.) for digital resources previously classified in theclass or category into which the digital resource identified by thedigital resource identifier may be classified. For example, in someembodiments, the relevance scorer may classify or match the digitalresource identified by the digital resource identifier based onclassification data within a predetermined time period. In someembodiments, the relevance scorer may generate, extract or identify oneor more keywords for or from the digital resource identified by thedigital resource identifier and match these one or more keywords tokeywords in the classification data and/or statistical analysis or modelfor the user specified by the user id.

The relevance scorer may generate or provide a relevance score 330responsive to an analysis or classification of digital resourceidentified by the digital resource identifier to the classification dataof the user identified by the user id. The relevance scorer may generatethe relevance score by any statistical calculation of the classificationof the digital resource identified by the digital resource identifierinto or using the classification data of the user specified by the userid. The relevance scorer may generate the relevance score by anytemporal weighting of temporal information of the classification data ofthe user specified by the user id. The relevance scorer may generate therelevance score using any of the scoring methods and techniquesdescribed elsewhere herein.

The relevance score 330 may comprise a value that provides an indicationof or otherwise identifies how relevant the digital resource identifiedby the digital resource identifier is to the user specified by the userid. The relevance score may be generated for or on an absolute orrelative scale. The relevance score may be generated for or normalizedto a predetermined relevance range, such as for example −100 to 100, 0to 100 or X to Y.

The relevance scorer may generate or provide a data score responsive to,in connection with or in conjunction to generating or providing therelevance score 330. The relevance scorer may generate or provide a datascore by any statistical calculation of an amount of cross-section ormatching in the classification data between user actions and globalactions associated with the user. The relevance scorer may generate orprovide a data score based on an amount or volume of data for the userin the classification data. The relevance scorer may generate or providea data score based on an amount or volume of data of user actions forthe user in the classification data. The relevance scorer may generateor provide a data score based on an amount or volume of data of globalactions for the user in the classification data. The relevance scorermay generate or provide a data score based on temporal qualities of thedata for the user in the classification data.

The data score 335 may comprise a value that provides an indication ofor otherwise identifies a quality of data supporting or underlying therelevance score. The data score 335 may comprise a value that providesan indication of or otherwise identifies a validity of the relevancescore based on a volume or quality of the classification data. The datascore 335 may comprise a value that provides an indication of orotherwise identifies a validity of the relevance score based on atemporal quality of the classification data. The data score 335 maycomprise a value that provides an indication of or otherwise identifiesan amount of cross-section or matching between user actions and globalactions associated with the user. The data score may be generated for oron an absolute or relative scale. The data score may be generated for ornormalized to a predetermined range, such as for example −100 to 100, 0to 100 or X to Y.

The linking system and/or relevance scorer may identify the user via theuser identifier. The user identifier 345 may comprise any type and formof identification of a user, such a name, alias, an account name, alogin name, or email address. The user identifier may be user identifierfor the user for using or accessing the linking system. The useridentifier may be user identifier for the user for using or accessing asocial networking site. The user identifier may be user identifier forthe user for using or accessing a third-party or partner web-site. Theuser identifier may be based on a cookie. The user identifier may bestored in a cookie. The linking system and/or relevance scorer mayidentify the user via one or more cookies.

Referring now to FIG. 3B, an embodiment of a method for scoring adigital resource 340 is depicted. In brief overview, at step 350, thelinking system receives user actions and at step 355, the linking systemreceives global actions. At step 360, a content extractor extracts oridentified keywords from content identified by or associated with adigital resource identified in any of the user actions and/or globalactions. At step 365, the classifier performs classification on theinput of user actions, global actions and keywords identified therein.At step 370, a relevance scorer receives a user identifier and a digitalresource identifier and generates a relevance score and data score.

In further details, at step 350, a server, such as the linking system,receives user actions from a plurality of users. The server may receiveuser actions via click streams 250′. The server may receive user actionsvia or comprising a user interacting, user or accessing a digitalresource. The server may receive users actions from the digitalresource. The server may receive a user action via a user requesting toencode an URL. The server may receive a user action via a user clickingon an encoded URL. The server may receive a user action via a request todecode an encoded URL. The server may receive and track user actionsover any period of time. The server may receive a user action via aclient linking system API or application 225. The server may receive oneor more user actions via a log file or activity log from an application,system, device or server. The click tracker may identify clicks of useron encoded URLs. The user tracker may identify the user who clicked theencoded URL. The user tracker may identify the user who encoded theencoded URL. The server may store the user actions associated with orattributed to each user into a database 230 and associated with oridentifiable via a user identifier.

At step 355, a server, such as the linking system receives globalactions. The server may receive global actions via a plurality of clickstreams 250′. The server may receive global actions via a user clickingon an encoded URL, such as encoded URL shared on a social networkingsite. The server may receive global actions via or comprising a usersharing a digital resource. The server may receive global actions fromthe digital resource. The server may receive one or more global actionsvia one or more log files or activity logs from an application, system,device or server. The user tracker may identify the user who shared theencoded URL. The click tracker may identify clicks of users on encodedURLs that have been shared. The user tracker may identify the user whoclicked the shared encoded URL. The server may store the global actionsassociated with or attributed to each user into a database 430 andassociated with or identifiable via a user identifier.

In some embodiments, steps 350 and 355 are provided or performed as asingle step. In some embodiments, steps 350 and 355 are provided orperformed in conjunction during the same step or sets of steps. Forexample, in some embodiments, the server may receive user actions andglobal actions via click streams received by the server. The server maytrack, manage and store any user and global actions on a per user basisin a database.

At step 360, a content extractor may identify keywords and/or phrasesfrom a digital resource, content identified by the digital resource orcontent otherwise associated with or corresponding to the digitalresource. The content extractor may operate responsive to receipt ofuser actions and/or global action. As the server receives a clickstream, the content extractor may identifier keywords or phrases fromthe click stream. In some embodiments, the context extractor obtains orfetches content corresponding to the digital resource. In someembodiments, the content extractor uses a predetermined list of phrasesand/or keywords to identify those phrases and/or keywords in the digitalresource, content identified by the digital resource or contentotherwise associated with or corresponding to the digital resource. Insome embodiments, the content extractor identifies keywords and/orphrases from predetermined locations or portions of a digital resourceor content associated with the digital resource. In some embodiments,the content extractor identifies keywords and/or phrases from useractions. In some embodiments, the content extractor identifies keywordsand/or phrases from global actions. In some embodiments, the contentextractor identifies keywords and/or phrases on a per user basis or foreach user.

At step 365, a classifier performs classification or otherwiseclassifies the keywords and/or phrases. The classifier may operateresponsive to the content extractor. As the content extractor identifiedkeywords or phrases, the classifier may receive these keywords orphrases and perform classification. The classifier may classify anycombination of keywords and/or phrases from the digital resourceidentifier, digital resource, content identified by the digital resourceor content otherwise associated with or corresponding to the digitalresource, user actions and global actions. The classifier may storeclassification data for each user. The classification data may representa classification of the user's interactions with digital resources intocategories or classes. The classifier may generate classification datathat represent a classification of the user's interactions with thedigital resources based on subject matter, interests or topics.

At step 370, the relevance scorer may receive a digital resourceidentifier and a user identifier. The relevance scorer may receive arequest to provide a relevance score for a digital resource or entityidentified by the digital resource identifier for a user identified bythe user identifier. Responsive to the request for a relevance score orreceipt of a digital resource identifier and a user identifier, therelevance scorer may generate, communicate or otherwise provide arelevance or content score. The relevance scorer may receive a requestto provide a data score for a digital resource or entity identified bythe digital resource identifier for a user identified by the useridentifier. Responsive to the request for a data score or receipt of adigital resource identifier and a user identifier, the relevance scorermay generate, communicate or otherwise provide a data score. In someembodiments, responsive to generating, communicating or providing arelevance or content score, the relevance score may also generate,communicate or otherwise provide a data score.

The relevance scorer may receive a digital resource identifier and auser identifier or a request from any application, system or server. Insome embodiments, a third party web site serving content may transmitthe request and/or digital resource identifier and a user identifier tothe linking system or relevance scorer. In some embodiments, an adserver serving advertisement or matching content to impressionopportunities may transmit the request and/or digital resourceidentifier and a user identifier to the linking system or relevancescorer. In some embodiments, a client application may transmit therequest and/or digital resource identifier and a user identifier to thelinking system or relevance scorer.

D. Systems and Methods for Providing Search Results Based on UserInteraction with Content

In some embodiments, the present disclosure is directed to systems andmethods for providing relevant real-time or static search results. Thesereal-time or static search results may be provided based on useractivity and/or engagement measurement from users with respect toparticular content and websites. In some embodiments, the linking system120 may provide or support interactive search based on global engagementpatterns of users, for example, across large internet platforms oracross multiple platforms. The search results may rank available contentbased on popularity of the content, which may be determined by uservisits and/or referring websites. These results may be determined basedon actual interactions between people and content. Such methods, whenincorporated into searches, may yield significant improvements overthose using typical link crawling and ranking based on links that resideon a page.

Referring to FIG. 4A, one embodiment of a system for providing searchresults based on user interaction with content is depicted. In briefoverview, the system includes a linking system 120 that incorporates arelevance system 405 for facilitating a search request. The relevancesystem 405 may receive a click stream 250′, as well as keywords orsearch terms 447 from a search request. In some embodiments, therelevance system 405 includes one or more of the following modules:distribution scorer 410, engagement scorer 415, frequency normalizer420, source weighting module 425, user weighting module 430, time decayfunction 435 and search relevance scorer 440. The relevance system 405may determine a relevancy score for each document identified in aclickstream, for ranking documents during a search.

In certain embodiments, the linking system 120, via a click tracker, maycollect, track or otherwise monitor information about links or contentaccessed by one or more users. The linking system 120 or the clicktracker may determine the content that users selected via encoded links,and may analyze the selected content to facilitate interactive search.By way of illustration, and in one embodiment, the linking system 120may prioritize content and/or links based on one or more of: (i) theidentity or other information of the user clicking on the link, (ii) theidentity or other information of the website providing the content orlink to the content, and (iii) the timing and/or number of usersaccessing the content via a certain link. In certain embodiments, thisprioritization may be determined or computed based on one or moremathematical and/or computer techniques, which may be custom orproprietary to the linking system 120. The linking system 120 mayestablish, maintain and/or update a database of content or links basedon the above determination, analysis and/or prioritization.

The database 230 may include any information or data associated with aclick on an encoded link. The database 230 may include data collected,tracked, and analyzed either statically or in real-time, and associatedwith the click. The linking system, via the click tracker, may analyzeand/or parse a clickstream for data to extract and store in the database230. In some embodiments, the database 230 may store data collected,tracked, and analyzed by the linking system 120 in response to amouse-over, copy or paste of an encoded link. The linking system maystore any portion of the collected data in a record of the database. Insome embodiments, a stored record may correspond to a click or otheruser action. The record may include any user and/or traffic dataprovided by a web browser, such as those described above in connectionwith FIGS. 2, 3A and 3B.

In some embodiments, a search engine or system is supported by thelinking system 120 in real time or substantially in real time. Thesearch system may change or update the relevancy of content for a useras that user and/or other users click on or navigate links (e.g.,encoded links) on a page. The relevancy of a content may be determinedin relation to keywords or search terms, user activity and/or activityfrom various websites. The search system may communicate with or rely onthe linking system 120 to process the clickstreams in real time orsubstantially real time. In some embodiments, the linking systemincludes a clickstream processing module, which may be referred to as arelevance system 405 or omniflector. The linking system 120 may processor decode a clickstream in real time, and may dissect, organize, bufferand/or store the processed information to the database 230.

In some embodiments, the database 230 may be referred to as aredistribution database or a sharded redis cluster database. Thedatabase 230 may comprise one or more sharded redis cluster databases,e.g., in a storage area network (SAN). In certain embodiments, therelevance system 405 may receive and/or process a portion of theclickstream to obtain a social score for each content or document(hereafter sometimes generally referred to as “document” or “content”).The relevance system 405 may perform calculations on a portion of thedecoded stream, to generate the social score and/or other data. Thelinking system 120 may store the social score and/or other data to thedatabase 230. In some embodiments, the linking system, e.g., via therelevance system 405, may receive or access a portion of the stored orbuffered information. The linking system 120 may use this information togenerate, create, calculate or otherwise determine a social score foreach document.

In some embodiments, a social score indicates, describes or representshow popular or in demand a certain document is. In some embodiments, asocial score is a weight to be applied to the relevancy scoring. Asocial score may be determined based on the number of times a documentis “clicked” or accessed, and/or how widely the document is being sharedor referred (e.g., a corresponding encoded link is forwarded or copied).The social score may be tempered by, or incorporate, a decay function toallow old content to decay, and new content to gain traction, prominenceor relevance in searches. The linking system 120 or relevance system 405may include a decode processor, which may comprise or execute anyapplication, algorithm, program, library, process, service, script, taskor any type and form of instructions for determining social scores. Insome embodiments, the decode processor includes a chip or a chipset,e.g., a CPU, an application specific integrated circuit (ASIC), aField-programmable gate array_(FPGA), or any other hardware fordetermining the social score.

By way of illustration, and not intended to be limiting in any way, thesocial score may be any real number or integer ranging between 1 and 100(or any other range). A maximum of 80 points for example, may be mapped,derived, or contributed from a raw social score. A maximum of 10 points,for example, may be mapped, derived, or contributed from a count of thetotal number of social referrers. A maximum of 10 points, for example,may be mapped, derived, or contributed from a count of the total numberof other referrers. The raw score may indicate the reach of a website ora document online, and may include a number of components, including adistribution score, an engagement score, etc. The raw score may have anyvalue larger than one (or zero, in some embodiments). A raw scorefalling within certain predefined range may be mapped to a value thatmakes up a portion (e.g., 80%) of the social score. The total number ofsocial and/or other referrers may similarly yield component value(s)that contribute to the final value of the social score. By way ofexample, one embodiment of a pseudo code or algorithmic description ofthe determination of a social score is depicted below:

a = raw social score [e.g., weighted number of clicks, weight mayindicate the reach of a site on the internet] b = total count of socialreferrers c = total count of other (e.g., non-social) referrers d =mapped social score [a: 1-400 -> buckets 1-70; a: 401-2000 -> buckets71-80, > 2000 = 80] e = mapped social referrers [b * 3, may be boundedbetween 0 and 10] f = mapped other referrers [c / 2, may be boundedbetween 0 and 10].

In some embodiments, the raw social score may contribute the largestcomponent of the social score. However, various different attributionweights may be implemented in various embodiments, and applied to eachof the social score components. In one embodiment, the linking system120 (e.g., via a source weighting module 425) may assign a weight (e.g.,ranging between 1 and 10) to certain websites (e.g., top sites) ordocuments in accordance with the reach they have on the web. Sites withhigh or broad reach, such as Facebook and Twitter, may be assigned aweight of 10 or some large value. Lower reaching sites, such asXing.com, may be assigned a 1 or some small value. In some embodiments,when a social click is identified or detected, the raw social score fora corresponding document is adjusted according to the appropriate,assigned weight. By way of example, a click from Facebook may be worth10 times as much as a click from Xing, and correspondingly representedby their assigned weights.

The raw social score of a document may continue to increase in value asthe document's link receives more clicks. In one illustrativeembodiment, the raw score may be mapped to a number between 1 and 80,e.g., with the bottom 70 points mapped to lower raw score ranges, andthe last 10 points mapped to higher raw score ranges. In someembodiments, the source weighting module 425 may receive source weighttraining data 427 to generate an initial social score, or to test thesocial scoring system. The source weight training data 427 may includeweights to assign to certain sites, and these weights may be adjusted orupdated as sites are ranked and re-ranked. In some embodiments, thesource weight training data 427 may include clickstreams (e.g.,simulated, historical, or received in real time) for evaluating sourceweighting and social scores.

In some embodiments, the relevance system 405 identifies users thattriggered the clickstreams and includes a user weighting module 430configured to weight the social score based on the identified users. Theuser weighting module 430 may assign specific weights to particularusers, such as those identified as influencers (e.g., showingsignificant social networking reach in sharing content, etc) and/orpower users (e.g., performing significant number of searches, which mayinfluence search relevancy in a positive or negative way for otherusers). By way of example, the user weighting module 430 may measure thevalue of particular users (e.g., a leading social networker on shoefashion trends) on specific keywords, search terms or search strings(e.g., “lace up boots”, “tasseled leather pumps”), and may categorizesuch users as influencers. The user weighting module 430 may weightsearch results based on identifier influencers, for example boostingsearch results for content that is either encoded or decoded byinfluencers. In certain embodiments, the user weighting module 430assigns weights to clicks associated with certain user data orcharacteristics (e.g., gender, age group, or users whose online historyinclude visits to websites catering to specific subject matter).

In certain embodiments, the total number or count of social referrersrepresents the total number of unique referring domains for a certaindocument or content. These referring domains may be social websites oronline social networks. The total number of other referrers mayrepresent the total number of unique referring domains that are notsocial sites, for a certain document or content. In the above example,the total number of social referrers may be multiplied by three, and theresult bounded between 0 and 10. The linking system 120 may divide thetotal number of other referrers by 2 (or some other number), and boundthe result between 0 and 10. Either of these referrer counts may beadjusted by some other predetermined multiplication (or division) factorbefore contributing to the social score. In addition, either of thesereferrer counts may be capped or bounded by other ranges (e.g., 0-100)after adjustment.

Any of the above information may be used to determine the social scoreand/or to determine which documents get indexed within search lists.Once a document is indexed, the relevancy score for a document isdetermined by a relevance system of the linking system 120 using acombination of word relevancy and the document's social score. Thesocial score may indicate that a document is relatively more important,or is more relevant to a search if the document has been accessed byusers and/or from domains (e.g., via encoded links) that have a lot ofinfluence or reach on the web. It may be expected that documentsaccessed from popular sites may be more popular and relevant to a userin a search. The linking system 120 may index each document identifiedin a clickstream. The linking system 120 may index or store the documentin the database 230 with or against a corresponding social score. Thissocial score of a document may be updated in real time, according to aschedule, or in response to a triggering event (e.g., a search). Incertain embodiments, the relevance system 405 and/or search system mayaccess, compare and/or use the social score of a document during asearch.

In some embodiments, the social score of a document is determined basedon social clicks that access the document via encoded links. In otherembodiments, the social score may incorporate one or more types ofclicks, which may include non-social clicks. In some embodiments, socialclicks include clicks on document links from identified top websites. Insome of these embodiments, social clicks include clicks on links fromtop social sites. The linking system may identify, maintain or track anumber of top sites, e.g., 1000 top sites, which may be provided bypartners, market research providers (e.g., Doubleclick), analytics (e.g.BlueKai and Exelate) or search providers (e.g., Yahoo, Google and Bing).In some embodiments, the top sites may comprise, exclusively ornon-exclusively, major social sites (e.g., Facebook, Twitter, Google+,LinkedIn, etc). The linking system 120 may determine that a clickreferred, redirected or consummated from one of these top sites ordomains may be considered a social click. As an illustration of therelative importance or relevance of social clicks, consider thefollowing: a linking system that focuses on social clicks from the top1000 sites may be able to index less than 1% of the domains beingclicked, but such clicks may account for over 38% of all online clicks.Thus, social clicks may be useful for determining social scores foronline content. In certain embodiments, the linking system 120 maydynamically monitor click counts from domains and determine top sitesbased on the distribution of click counts. The linking system maydynamically identify top sites, e.g., in lieu of a static list of topsites provided by a third party.

In certain embodiments, the linking system may classify or group two ormore clicks on a social site for the same document, occurring within acertain time period (e.g., 2 or 5 minutes), as a social click. In someembodiments, the linking system may group multiple clicks from othertypes of websites as a single social click. Documents identified by asocial click may be included in a directory, database, search list,index or application programming interface to index the document forsearch. In some embodiments, such documents are placed in apublisher/subscriber (pubsub) index or stream that can be pulled duringsearches. A search engine may pull the social clicks pubsub stream andmay continuously index global hashes within the pubsub. Documents thatare popular may tend to be clicked over and over again. Such documentsmay get indexed again and again corresponding to the clicks. Asdocuments' links are clicked, the linking system 120 may update thedocuments' social scores. As the documents get re-indexed, theircorresponding social scores may be higher, resulting in higher rankingswithin search results.

In certain embodiments, the number of social clicks may contribute to asocial score in a linear or non-linear fashion. For example, a socialscore of a document may be configured such that it is directlyproportional to the number of social clicks detected. In some otherembodiments, the first clicks (click number 1-80) from a social websitemay be weighted more than later clicks (click number 81 to 100) indetermining a social score. In one embodiment, and by way ofillustration, the number of social clicks may be represented as follows:

${sc}_{i,j} = {{\sum\limits_{1,80}\;\frac{s(i)}{10}} + {\sum\limits_{81,100}\;\frac{s(i)}{100}}}$

In some embodiments, s(i) may represent the click count for term iwithin the respective click count ranges for document j. Additionalclicks (e.g., beyond 100) may be weighted less or differently, ordisregarded after a certain threshold (e.g., 100 clicks). In certainembodiments, the number of social clicks, sc_(i,j), may be referred toas an engagement score. An engagement scorer 415 of the linking systemmay rank, score or rate content based on how many clicks are received.An engagement scorer 415 may include or execute any application,program, library, process, service, script, task or any type and form ofexecutable instructions for generating and/or updating an engagementscore. The engagement scorer 415 may apply the above function, or someother nonlinear function, such that initial clicks are weighted morethan later clicks. The engagement scorer 415 may emphasize initialclicks so that new content may promoted over older content with the samefrequency of clicks. The linking system 120 may, for example, promotenew content by leveraging on a time decay function in conjunction withthe engagement score. The linking system 120 may include or incorporatethe engagement score in a social score.

As described, the linking system 120 may calculate the social score foreach document, and may stored the social score in an index. The socialscore may be decayed, reduced or de-emphasized in time for use insearches, e.g., from the time the document is first indexed. A socialscore may decay down to half of its original score over a configuredperiod of time, such as 72 or 168 hours. In some embodiments, a socialscore may be decayed as a function of e^(x) (e.g., f(e^(x))), where xrepresents time. The linking system 120 and/or search system may use thesocial score to determine a relevancy score. The linking system 120 mayinclude a search relevance scorer 440 for determining and/or updatingthe relevancy score of a document. The search relevance scorer 440 mayinclude and/or execute any application, program, library, process,service, script, task or any type and form of executable instructionsfor generating and updating the relevancy score of a document. Thesearch relevance scorer 440 may communicate or interoperate with one ormore modules (e.g., distribution scorer 410, engagement scorer 415,frequency normalizer 420, time decay function 435) in determining and/orupdating a relevancy score. A relevancy score may sometimes be referredto as a search score or a relevance score. The social score may be oneof several factors that determines the relevancy score of a document aspertains to search (e.g., interactive search).

In some embodiments, a relevancy score is based on one or more ofweights, factors or components, for example: (i) Social score (e.g., howpopular a document is), (ii) time decay (e.g., how long a document hasbeen in the index), and (iii) normalized frequency (e.g., how relevantcertain terms are to the text of a document). Other weights, factors orcomponents may include (iv) a distribution score and (v) an engagementscore. In certain embodiments, the social score for a document mayincorporate one or more of the above components, e.g., time decay,distribution score and/or engagement score. For example, the socialscore may be subject to time decay prior to being used by the linkingsystem 120 to determine a relevancy score. In other embodiments, thesocial score may be combined with other factors before being subject totime decay to establish the relevancy score. As discussed, time decaymay incorporate an e^(x) function with respect to time (x), or anylog-linear time decay function. With the time decay function, newdocuments with links that are more recently clicked may have a highersocial and/or relevancy score.

A distribution scorer of the relevance system may calculate or determinethe distribution score or weight for a document. The distribution scorer410 may include any application, program, library, process, service,script, task or any type and form of executable instructions formonitoring or tracking clicks arising from various websites. Thedistribution scorer 410 may determine that a document is ranked higheror more relevant in a search if clicks to access such a document arisefrom a broader set of source web sites. The distribution scorer 410 maydetermine the distribution of clicks (e.g., counts of social clicks ornormal clicks) for the same document across a plurality of sites. Insome embodiments, the distribution scorer 410 may determine the numberof source sites (e.g., specific social sites) that referred clicks for acertain document. In certain embodiments, the distribution score may bereferred as “social distribution”. The distribution score may becalculated, expressed or represented in one embodiment by the followingformula:

${sd}_{ij} = {\max\left( {{\sum\limits_{k}\; n},{Max\_ SD}} \right)}$where $\sum\limits_{k}\; n$can represent the number of source sites that have been referring clicksfor a document. In some embodiments,

$\sum\limits_{k}\; n$represents the summation of website counts, e.g., over k categories ofclicks or k types of websites. Max_SD may be an upper bound or cap forthe distribution score (e.g., 100 websites). In some embodiments, Max_SDis optional, and may be set to 0. Where Max_SD is specified, thedistribution score may take the larger of Max_SD or the number of sourcesites referring clicks for the document.

In some embodiments, the linking system 120 includes a frequencynormalizer 420. A frequency normalizer 420 may include any application,program, library, process, service, script, task or any type and form ofexecutable instructions for processing a term or word's frequency ofoccurrence in a document. Once enough traffic has been monitored forclickstream activity, the linking system 120 may retrieve a documentidentified in the clickstream, using the corresponding long URL. Thelinking system may extract content that is appropriate for text-based,keyword search. The frequency normalizer 420 may extract key words fromthe extracted content, and may normalize some of these keywords. In someembodiments, the frequency normalizer 420 may insert or store thekeywords and their normalization values into the database 230.

In some embodiments, the frequency normalizer 420 uses a termfrequency-inverse document frequency (tf/idf) ratio or metric to obtainthe normalization values. This metric may be used as a statisticalmeasure to evaluate the importance of a term or word within a document.The importance of a term may increase proportionally to the number oftimes the term appears in the document. The importance of the term maybe offset by the frequency of the word within a superset of documents(e.g., corpus) comprising the document. The linking system may use thetf/idf metric to score or rank a document's relevance in a given searchquery. The linking system may use the tf/idf metric to normalize ordiminish the weight of terms (e.g., “the”, “of”) that occur veryfrequently in the corpus and increase or normalize the weight of terms(e.g., “hibernation”, “omnibus”) that occur rarely.

In some embodiments, the tf/idf metric comprises a term frequency (tf)and the inverse document frequency (idf) components. The tf componentmay represent the occurrence count of a term in a document. The tfcomponent may be determined from the number of times a given termappears in that document, normalized to prevent a bias towards longerdocuments to give a measure of the importance of the term i within theparticular document j. For example, in a document containing 100 wordswherein the word brown appears 3 times, the tf value for brown may bedetermined as (3/100)=0.03. In some embodiments, the tf value isexpressed in logarithmic form, e.g., log(0.03). One embodiment of the tfvalue is presented as follows:

${{tf}_{i,j} = \frac{n_{i,j}}{\sum\limits_{k}\; n_{k,j}}},$where k may represent the number of distinct terms in the document.

In some embodiments, the idf component is a measure of the generalimportance of a term. It may be obtained by dividing the total number ofdocuments by the number of documents containing the term, and thentaking the logarithm of that quotient. One embodiment of the idf may berepresented as follows:

${{idf}_{i,j} = {\log\left( \frac{D}{\left\{ {{j\text{:}t_{i}} \in d_{j}} \right\} } \right)}},$where D may represent the corpus or set of all documents, |D| mayrepresent the cardinality of D, or the total number of documents in thecorpus. |{j:t_(i)εd_(j)}| may represent the number of documents wherethe term t_(i) appears. By way of example, if there are 10 milliondocuments and the term brown appears in one thousand of these, theinverse document frequency may be calculated as log(10,000,000/1,000)=4.

The normalization frequency of a document term may be determined as theratio of tf to idf, e.g., tf/idf. In one embodiment, this is representedas:

$\left( {{tf}/{idf}} \right)_{i,j} = {{{tf}_{i,j}/{idf}_{i,j}} = \frac{\frac{n_{i,j}}{\sum\limits_{k}\; n_{k,j}}}{\log\frac{D}{\left\{ {{j\text{:}t_{i}} \in d_{j}} \right\} }}}$

In some embodiments, the search relevance scorer 440 may use the tf/idfnormalization frequency to determine the relevancy score. However, insome other embodiments, the search relevance scorer 440 may use adifferent document frequency or a variant of the tf/idf normalizationfrequency. For example and in one embodiment, the search relevancescorer 440 may use a td·idf weight, which may be represented as:

$\left( {{tf} \cdot {idf}} \right)_{i,j} = {{{tf}_{i,j} \times {idf}_{i,j}} = {\frac{n_{i,j}}{\sum\limits_{k}\; n_{k,j}} \times \log\frac{D}{\left\{ {{j\text{:}t_{i}} \in d_{j}} \right\} }}}$

In certain embodiments, the frequency normalizer 420 may compute ordetermine a td-idf value using Lucene scoring methods. A Lucene score,may, for example, be expressed in the following embodiment:

$\begin{matrix}{{score} = {{td} - {{idf}\mspace{14mu}{value}}}} \\{= {{coord} \cdot {queryNorm} \cdot {\sum\limits_{i\mspace{14mu}{in}\mspace{14mu} q}\;\left( {{td} \cdot {idf}^{2} \cdot {Boost} \cdot {norm}} \right)}}}\end{matrix}$where coord may be a score factor based on how many of the query termsare found in the specified document. A document that contains more ofthe query's terms may receive a higher score than another document withfewer query terms. queryNorm may be a normalizing factor for makingscores between queries comparable. Boost may represent a search timeboost of one or more terms in the query as specified in the query text.Boost may be used to access a boost of one or more terms in a multi termquery. norm may include one or more boost and length factors, such asboost factors for specific documents, fields in a document, fieldlength, etc.

In various embodiments, the relevancy scorer 440 may apply differentvariants of Lucene scoring and the td-idf value in determining therelevancy score. In certain embodiments, for example, different fieldsin a document may carry different weights. The relevancy scorer 440 mayconfigure these weights as custom properties for a search, a documentindex, the relevancy scorer 440 and/or frequency normalizer 420 forexample. In one embodiment, the relevancy scorer 440 may confer weightsor boosts to the following content fields: title=5, meta keywords=3,meta description=4, meta site=4, domain=4, url=3, page=1, globalhash=5,h1=7, h2=5, h3=3, h4=2, cities=3. These may be configured by anadministrator and/or determined based on search activity. In someembodiments, arbitrary weights may be assigned and updated based onsearch activity. By way of illustration, the following is a listing ofconfigured document field boosts:

<entry key=“boosts.site”>4.0</entry> <entrykey=“boosts.domain”>4.0</entry> <entry key=“boosts.url”>3.0</entry><entry key=“boosts.globalhash”>5.0</entry> <entrykey=“boosts.phrase”>5.0</entry> <entry key=“boosts.title”>5.0</entry><entry key=“boosts.description”>4.0</entry> <entrykey=“boosts.keywords”>3.0</entry> <entry key=“boosts.lang”>2.0</entry><entry key=“boosts.h1”>7.0</entry> <entry key=“boosts.h2”>5.0</entry><entry key=“boosts.h3”>3.0</entry> <entry key=“boosts.h4”>2.0</entry><entry key=“boosts.cities”>3.0</entry>

Certain variants of Lucene scoring may incorporate boosts values foreach document field. For example, Lucene scoring may apply aconfiguration in which text in h1 tags are more important than those inh2 tags and the title field, which may be more important than the domainfield, etc. A document or content may be configured for a relativeboost. In certain embodiments, a document or content may not beconfigured for a boost, although it may incorporate a score or weight inthe Lucene scoring method based on the length of text, number of termsin the document and other factors, for example.

In some embodiments, the search relevancy scorer 440 determines therelevancy or search score with the following computation: time-decayedsocial score*tf-idf value. In another embodiment, the search relevancyscorer 440 may determine the relevancy score using the followingformula:Σ(decay time·Πtd-idf·social distribution score·social score),where Π may represent a direct or Cartesian product of td-idf values.Yet other embodiments may incorporate any of the components described,e.g., engagement score, user weighting, source weighting, etc. Therelevancy scorer may rank or index documents based on any variant orcombination of relevancy scores, and the highest scoring documents maybe returned first in a search.

By way of illustration, a retrieval mechanism may be used to query thedatabase, apply the relevancy score (or a combination of scoresdescribed above), and provide a set of search results 450 ordered by howrelevant they are in the clickstream. The relevance system 405 mayreceive keywords or search terms 447 from a search and may process theseinto a search relevance query 445. Search terms may include one or moreparameters that identify or define audience or user segments. These oneor more parameters may break down, identify or define users intosub-groups, such as by demographics, communication behavior and mediause. In some embodiments, a search term may identify a geography scopeor limitation, such as limiting the search to users who live in Italy.In some embodiments, a search term may identify a language scope orlimitation, such as limiting the search to users who read content inItalian or interacted or clicked on content in Italian. In someembodiments, a search term may identify an influence rating.

The search relevance query 445 may for example, comprise scores forkeywords, indexes into specific groups of documents and/or informationabout the user. The relevance system 405 may apply the search relevancequery 445 against the indexed documents, which are ranked by theirrelevance scores. By matching document relevance against the query 445,the relevance system 405 may return one or more documents in the searchresults. The system 405 may return search results limited to or based onany of the audience or user segmentation terms. For example, if ageography term of Italy and a language term of English, the system mayreturn search results based on English based content interacted with,encoded or clicked on by users locating in Italy.

Although some components or factors may be generally referred to ordescribed herein as scores or scoring, these components or factors maybe considered weights to be applied to the relevancy calculation oralgorithm. For example, a social score, a distribution score, anengagement score, a frequency normalization and/or a time decay functionmay be considered weights or weighting factors for the relevancy systemand may be applied to weight the relevancy score or provide a weight toother components or inputs of the relevancy system.

Referring to FIG. 4B, one embodiment of a method for providing searchresults based on user interaction with content is depicted. In briefoverview, the method includes receiving, by a server, identification ofa plurality of clicks of encoded uniform resource locator (URL) links(455). The server may identify, for each of the plurality of clicks,data about a user who clicked an encoded URL link and traffic dataassociated with a device from which the user clicked the encoded URLlink (460). The server may store a record for each click of theplurality of clicks, the record comprising data about the user andtraffic data associated with each click (465). The server may determine,based on the records, a relevancy score for each content identified fromdecoding the encoded URL links (470). The server may communicate,responsive to receiving a request to search content based on a keyword,a set of search results based on the keyword and the relevancy score(475).

Referring now to (455), a server may receive an identification of aplurality of clicks of encoded uniform resource locator (URL) links. Theserver may comprise any embodiment of the linking system 120 describedabove in connection with FIGS. 2, 3A and 4A. The server may decode eachof the encoded URL links, for example, as described above in connectionwith FIG. 2. The server may decode an encoded URL link in real time. Theserver may redirect the click to access a document from another URL.

In further details of (460), the server may identify, for each of theplurality of clicks, data about a user who clicked an encoded URL linkand traffic data associated with a device from which the user clickedthe encoded URL link. In some embodiments, the server may identify dataabout the user from a cookie communicated via a click by the user on theencoded URL link. The server may identify, for example, traffic datacomprising one or more of a browser type, a referring web site, a sourceinternet protocol address, a destination internet protocol address, atime instance of a click, a document accessed.

The server may identify any other data about the user and/or trafficdata as described above in connection with FIGS. 2 and 4A. For example,the server may determine if a user is an influencer or meets certaincriteria for increased weighting. The server may assign an appropriateweight to this user's social clicks, e.g., in determining a document'ssocial score. In some embodiments, the server may determine, based ontraffic data, if a number of clicks are social clicks that maycontribute to a social score, or if a click is triggered from a websiteidentified as a top site and/or a social website. The server may assigna greater weight to social clicks arising from a top site and/or asocial website. In certain embodiments, the server may track the numberof corresponding clicks from a document, and the distribution of sourcewebsites that triggered the clicks.

Referring now to (465), the server may store a record for each click ofthe plurality of clicks. The server may store the record in anyembodiment of the database 230 described above in connection with FIGS.2 and 4A. The record may comprise any form of data structure such as adatabase or hash entry, a table item, or any collection of dataassociated with a click, document/content, user and/or source website.The record may comprise data about the user and traffic data associatedwith each click. In some embodiments, the server may store an index of adocument identified by a click or clickstream. The server may store,maintain and update an index of documents to facilitate searches forcontent. The server may, in some embodiments, retrieve and store adocument identified via a clickstream in the database 230. In certainembodiments, the server may determine, rank and store a list of topwebsites based on where the clickstreams arose. The record may includefields or memory space for storing one or more scores, such a socialscore and/or a relevancy score for a document.

Referring now to (470), the server may determine, based on the records,a relevancy score for each content identified from decoding the encodedURL links. The server may determine the relevancy score via one or moremodules, for example, a search relevancy scorer 440, a frequencynormalizer 420, a distribution scorer 410 and an engagement scorer 415.In some embodiments, a distribution scorer 410 of the server maydetermine a distribution score for each content based on a number ofclicks from different sources via one or more encoded URL links to thecontent. The distribution scorer 410 may determine that a document has ahigher distribution score, or is more relevant to a search if clicks toaccess such a document arise from a broader set of source web sites. Thedistribution scorer 410 may determine the distribution of clicks for thesame document across a plurality of sites. The distribution scorer 410may track or evaluate the number of source sites that referred clicksfor a certain document.

In certain embodiments, the server may determine, via an engagementscorer 415, an engagement score for each content based on a number ofclicks received via one or more encoded URL links to the content. Eachof the number of clicks may be weighted based on when the click wasreceived. The engagement scorer 415 may determine the engagement scoreof a document based on the number of social clicks made to access thedocument. The engagement scorer 415 may rank content based on how manycorresponding clicks are received. In certain embodiments, theengagement scorer 415 applies a nonlinear function to click counts suchthat initial clicks are weighted more than later clicks.

In some embodiments, the server may determine, via a frequencynormalizer 420, a frequency normalization value for each content. Thefrequency normalizer 420 may extract keywords from the content,normalize the keyword counts and may store the keywords andcorresponding normalization values into a database (e.g., database 230).The frequency normalizer 420 may extract content from a document, suchas text-based content, suitable for text-based keyword searches. Thefrequency normalizer 420 may determine each term or word's frequency ofoccurrence in the extracted content. The frequency normalizer 420 maynormalize the counts of these terms or words. In some embodiments, thefrequency normalizer 420 may use td-idf normalization, such as Lucenescoring, to perform the keyword normalization. The frequency normalizer420 may provide a td-idf value for each document, to determine therelevancy score of the document.

In certain embodiments, the server may apply a time decay function tothe relevancy score based on the length of time a content has beenstored in a record after being identified from decoding the encoded URLlinks. In some embodiments, the time decay function is incorporated intothe calculation or formula for determining the social score or therelevancy score, for example, as described above in connection with FIG.4A. The time decay function may incorporate a log-linear time functionin certain embodiments. The time decay function may enable a documentwith links that are more recently clicked to have a higher social and/orrelevancy score.

The server may determine, via a search relevance scorer, the relevancyscore of a document by a combination of two or more of a social score, adistribution score, an engagement score, a frequency normalizationvalue, a time decay function, a source weighting component and a userweighting component. The search relevancy scorer may rank or indexdocuments based on any one or a combination of these scores or values.For example, the search relevancy scorer may use relevancy scores toreturn the highest scoring documents in a search, e.g., based on theclosest matching keywords.

In further details of (475), the server may communicate, responsive toreceiving a request to search content based on a keyword, a set ofsearch results based the keyword and the relevancy score. The server mayreceive one or more keywords or search terms from a user or applicationbased on a search. In some embodiments, the server The server mayprocess the one or more keywords or search terms into a search relevancequery 445, which may include a processed set of terms and/or priorityapplied to each of the processes terms. The search relevance query 445may comprise scores for keywords, indexes into specific groups ofdocuments and/or information about the user. In some embodiments, theserver may receive the search relevance query 445 from a search engineor an application. The server may apply or match one or a combination ofkeywords or terms against a listing, database or index of documents. Thelisting, database or index of documents may be ordered, ranked orindexed based on the documents' relevancy scores and/or document terms.The server may return a set of search results or documents based on therelevancy scores and/or closest matching terms of the documents. In someembodiments, the server may order the set of search results based onrelevancy score of the documents.

In certain embodiments, the server may match a plurality of documentsagainst one or a combination of keywords or terms without using therelevancy scores. The server may identify a subset of documents thatmost closely matches the one or a combination of keywords or terms. Theserver may rank this subset of documents based on the how closely thedocuments matches the one or a combination of keywords or terms. In someembodiments, the server combines this ranking with the relevancy scoreof these documents, to order the set of search results. The server may,for example, reorder the set of search results based on the relevancyscores or by applying a weighted preference based on the relevancyscores. Using embodiments of the above processes, the server maygenerate and/or order search results based on the relevance of thedocuments, e.g., as determined by user interaction, user feedback,and/or based on the popularity of particular content in connection withsocial media.

E. Systems and Methods for Identifying Trends in Phrases of Content

Referring now to FIGS. 5A and 5B, systems and methods for identifyingtrends in phrases in content is depicted. These systems and methods maydetermine trending or popular phrases from user interactions with webcontent containing, related to or associate with such phrases. Thesesystems and methods identify trending or temporally popular phrasesbased on aggregating multiple users' interactions with an aggregate ofcontent. Using click stream data and any list of phrases, ontology, ordictionary, systems and methods of the present solution can score webcontent based on users level of engagement with such content, and deducethe most popular phrases being viewed across a large set of content.These systems and methods may be applied in real-time or statically.These systems and methods may generate or provide a list of phrases ortopics that are trending upwards and/or downwards.

Referring now to FIG. 5A, an embodiment of a trending system 505 foridentifying trends in phrases related to, contained in, describing orotherwise associated with content for which users interact with or clickon is depicted. In brief overview, the system 505 includes a keywordextractor 515 that identifies phrases 520 from content identified via aclick stream 250′. The keyword extractor may also identify phrases 520from a predetermined set of web-sites 550. The keyword extractor mayoperate responsive a predetermined phrases list 522 by matching phrasesfound in content to this list. A trending engine 525 may receive thesephrases 520 as input and determine which phrases are trending up and/ordown or which phrases or topics are most popular. The trending enginemay include a velocity engine or component 530. The velocity engine maydetermine a number of clicks over a predetermined period of time for anyone or more phrases across any one or more sites. The trending enginemay generate an enumeration or list of phrases 540 that are trendingupwards and/or downwards. The list of phrases may be ranked according tothe change in trending, popularity or other criteria.

In further detail, the keyword extractor 515 may comprise anapplication, program, library, process, service, script, task or anytype and form of executable instructions for identifying, extracting, ordetermines keywords and/or phrases from, in, related to, describing orassociated with content, such as content the user clicked on an encodedURL link. The keyword extractor 515 may comprise any embodiments of thecontent extractor 315 described above in connection with FIG. 3A. Thekeyword extractor 515 may operate responsive to a phrases list 522. Insome embodiments, the keyword extractor may be configured with thephrases list. In some embodiments, the keyword extractor may read orprocess the phrases list from a file, data object or table of adatabase. The keyword extractor may be designed and constructed toidentify or detect keywords and/or phrases from the phrases list incontent from or of a digital resource, such as a web page identified bya URL.

The phrases list 522 may comprise any data and information identifying apredetermined set of phrases and/or keywords. The phrases list maycomprise a dictionary. The phrases list may comprise an ontology. Thephrases list may comprise an enumerated list of phrases and/or keywords.The phrases list may comprise an enumerated list of phrases and/orkeywords ranked in order of priority or otherwise having an identifiedpriority. The phrases list may comprise an enumerated list of phrasesand/or keywords ordered based on ranking or otherwise having anidentified ranking. The phrases list may comprise an enumerated list ofphrases and/or keywords with assigned weights or weighting. The phraseslist may identify a predetermined list of topics, interests or subjectmatter. The phrases list may identify a predetermined set of keywordsrelated to or making up a topics, interests or subject matter. Thephrases list may be generated from a third-party source, such as aweb-site or URL. The phrases list may be generated by the trendingengine based on a count of phrases and/or keywords identified in thepredetermined list of web-sites. The phrases list may be generated fromprevious versions of the phrases list. The phrases list may be generatedbased on learning or intelligence of the trending engine.

The keyword extractor may identify keywords responsive to one or moreclick streams 250′. In some embodiments, the keyword extractor operatesresponsive to receipt of a click stream or click action. In someembodiments, the keyword extractor operates in real-time as aclick-stream or portions thereof are received by the system 120. In someembodiments, the keyword extractor operates responsive to receipt of abatch of click streams or click actions. In some embodiments, thekeyword extractor operates responsive to a predetermined frequency,which may be configurable. In some embodiments, the keyword extractoroperates independently from the click stream and identifies keywordsfrom a predetermined set or list of web-sites 550. In some embodiments,the keyword extractor identifies keywords from a predetermined set orlist of web-sites 550 on a predetermined frequency. In some embodiments,the keyword extractor identifies keywords from a predetermined set orlist of web-sites 550 responsive to an event, such as a user request. Insome embodiments, the keyword extractor operates responsive to aclick-stream while identifying keywords from a predetermined set or listof web-sites 550.

The list or set of web-sites 550 may include an enumeration orconfiguration of a predetermined set or list of URLs, web-sites ordigital resources. The list or set of web-sites 550 may include a listof the most popular web-sites or URLs. The list or set of web-sites 550may include a list of the most visited web-sites or URLs. The list orset of web-sites 550 may include a list of the frequently visitedweb-sites or URLs. The list or set of web-sites 550 may include a listof the highest ranked web-sites or URLs. The list or set of web-sites550 may include a list of the most searched web-sites or URLs. The listor set of web-sites 550 may include a list of web-sites or URLs selectedby a user. The list or set of web-sites 550 may change based on changesin the ranking of any of these web-sites or URLs. The keyword extractormay be configured with the predetermined set or list of web-sites. Thekeyword extractor may be designed and constructed to read or process adata file, object or table of a database with the predetermined set orlist of web-sites. In some embodiments, the list of web-sites 550comprise a list of N (e.g. 1000) top sites on the internet by reach.This may be identified or pulled from Doubleclick's Top Sites[http://www.google.com/adplanner/static/top1000/] and may include all ofthe major social sites such as Facebook, Twitter, etc.

The keyword extractor may be designed and constructed to inspect, reador otherwise process any portion of content and match such portions tothe phrases list. The keyword extractor may strip images and/or othernon-textual elements from the content. The keyword extractor maysubtract common words from the textual portions of the content. Thekeyword extractor may be designed and constructed to inspect, read orotherwise process any text of content and match such text to the phraseslist. The keyword extractor may be designed and constructed to inspect,read or otherwise process any meta-data of content and match any stringsor text such meta-data to the phrases list. The keyword extractor may bedesigned and constructed to inspect, read or otherwise process any tags,scripts or mark-up language of content and match any strings or text ofsuch tags, scripts or mark-up language to the phrases list. The keywordextract may be designed and constructed to identify which phrasesdeviate from a norm relative to other phrases in the content.

The keyword extractor may be designed and constructed to generate,output or provide a set of phrases 520. The keyword extractor may bedesigned and constructed to interface to or communicate with thetrending engine 525. The keyword extractor may enumerate a set ofphrases and/or keywords based on a number of instances of the phraseand/or keyword. The keyword extractor may enumerate a set of phrasesand/or keywords based on a number of instances of the phrase and/orkeyword in the click stream. The keyword extractor may enumerate a setof phrases and/or keywords based on a number of clicks related to thephrase and/or keyword in the click stream. The keyword extractor mayenumerate a set of phrases and/or keywords based on a velocity of clicksrelated to the phrase and/or keyword in the click stream. The keywordextractor may enumerate a set of phrases and/or keywords based on anumber of instances of the phrase and/or keyword in the web-sites 550.The keyword extractor may enumerate a set of phrases and/or keywordsbased on a number of instances of the phrase and/or keyword in both theclick stream and in the web-sites. The keyword extractor may enumerate aset of phrases and/or keywords based on an order or ranking from thephrases list 522. The keyword extractor may enumerate a set of phrasesand/or keywords based on a corresponding weighting from the phrases list522. The keyword extractor may enumerate a set of phrases and/orkeywords based on temporal information. The keyword extractor mayenumerate a set of phrases and/or keywords on a real-time basis as theyare generated. The keyword extractor may enumerate a set of phrasesand/or keywords on a predetermined basis, such as on a predeterminedschedule or at a predetermined frequency.

The keyword extractor may filter the list of phrases based on ranking,priority or weighting, such as may be specified by the phrases list. Thekeyword extractor may filter the list of phrases based on apredetermined threshold, such as a number of instances of identificationof the phrase across content. The keyword extractor may filter the listof phrases based on temporal information and thresholds, such as anumber of instances of identification of the phrase across content overa predetermined time period. The keyword extractor may filter the listof phrases based on geography. The keyword extractor may filter the listof phrases based on user profiles. The keyword extractor may filter thelist of phrases based on source, such as via click streams or via thepredetermined web-sites.

The trending engine 525 may comprise an application, program, library,process, service, script, task or any type and form of executableinstructions. The trending engine may comprise functions, operations orlogic to identify trends in phrases and/or keywords across digitalresources interacted with by users, such as via clicking on encodedlinks to content related to, described by or containing the phrasesand/or keywords. The trending engine may be designed and constructed toprocess the phrases 520 from the keyword extractor and to determinewhich of those phrases are trending up and/or down based on userinteractions, such as clicking, with digital resources associated with,connected to or comprising those phrases. The trending engine may bedesigned and constructed to identify which phrases deviate from a norm.The trending engine may be designed and constructed to process thephrases 520 from the keyword extractor and to determine which of thosephrases are most popular. The trending engine may be designed andconstructed to process the phrases 520 from the keyword extractor and todetermine which phrases a user or set of users interact with the mostand/or the least. The trending engine may be designed and constructed toprocess the phrases 520 from the keyword extractor and to determinewhich of those phrases are from content of an encoded URL that usershave clicked on the most and/or the least. The trending engine may bedesigned and constructed to process the phrases 520 from the keywordextractor and to determine which of those phrases are from content of anencoded URL that users have shared the most and/or the least. Thetrending engine may be designed and constructed to process the phrases520 from the keyword extractor and to determine which of those phrasesare from content of a URL or web page that has been visited or servedthe most and/or the least.

The trending engine may comprise any embodiments of the relevance system405 described in connection with FIGS. 4A and 4B. For example, thetrending engine may comprise any embodiments of the distribution scorer410, engagement scorer 415 and/or time decay function 435 described inconnection with FIGS. 4A and 4B. Any embodiments of distribution scorer410, engagement scorer 415 and/or time decay function 435 may applied tocontent associated with or comprising the phrases and based on theresults the trending engine determines the trending phrases. Forexample, in some embodiments, the trending engine determines thosephrases from or associated with content with the highest raw socialscore. In some embodiments, the trending engine determines those phrasesfrom or associated with content with the a raw social score greater thana threshold. In some embodiments, the trending engine determines thosephrases from or associated with content with the a mapped social scoregreater than a threshold. In some embodiments, the trending enginedetermines those phrases from or associated with content with the atotal social score greater than a threshold. In some embodiments, thetrending engine determines those phrases from or associated with contentwith the highest number of social referrers or a total number of socialreferrers greater than a threshold. In some embodiments, the trendingengine determines those phrases from or associated with content with thehighest number of mapped social referrers or a total number of mappedsocial referrers greater than a threshold. Any of the scores from thetrending engine, such as via the distribution and/or social scorer, maybe decayed via the time decay function and the resulting score ofcontent used to identify the trending phrases accordingly.

The trending engine may be designed and constructed to determine avelocity of interaction with content associated with, related to orcontaining the phrases. The trending engine may determine such velocityvia a velocity engine or component. A velocity engine 530 may comprisean application, program, library, process, service, script, task or anytype and form of executable instructions. The trending engine mayinclude the velocity engine. In some embodiments, the velocity engine isseparate from the trending engine and the trending engine maycommunicate with or interface to the velocity engine. The velocityengine may be designed and constructed to determine any change in therate of interaction over time with content associated with, related toor contains with one or more phrases. The velocity engine may bedesigned and constructed to determine and/or track a number of clicks onan encoded URL over a predetermined time period in which the contentfrom or of the encoded URL is associated with, related to or containsthe phrase. The velocity engine may be designed and constructed todetermine and/or track a number of clicks on a plurality of encoded URLsover a predetermined time period in which content from the plurality ofencoded URLs is associated with, related to or contains the phrase. Thevelocity engine may be designed and constructed to determine and/ortrack the velocity of upward or downward trends of a phrase. Thevelocity engine may be designed and constructed to determine and/ortrack the velocity of popularity of a phrase. The velocity engine may bedesigned and constructed to determine and/or track the velocity ofserving or visiting content comprising a phrase.

The trending engine may generate, output, communicate or otherwiseprovide a list or set of one or more trending phrases 540. The output540 may be an enumerated list or ordered list. The output may be areport. The output may be a file. The output may be data stored in adatabase. The output may be a web page comprising the trending phrases.The output may be any digital resource comprising or identifying thetrending phrases. The trending engine may output the set of trendingphrases via an API call, event or function to an application, program orsystem. For example, the trending engine may output the set of trendingphrases via XML. The trending engine may output the set of trendingphrases via a web service call or response to a web service call. Thetrending engine may output the set of trending phrases via raising anevent or calling a function.

The output may be an encoded URL identifying a digital resourcecomprising or identifying the trending phrases. In some embodiments, thetrending phrases or output 540 comprises a list of phrases that aretrending upwards. In some embodiments, the trending phrases or output540 comprises a list of phrases that are trending upwards above, belowor within a predetermined threshold. In some embodiments, the trendingphrases or output 540 comprises a list of phrases that are trendingdownwards. In some embodiments, the trending phrases or output 540comprises a list of phrases that are trending downwards above, below orwithin a predetermined threshold. The trending phrases or output 540 maybe in ascending or descending order.

In the output 540, the trending engine may identify for each or some ofthe phrases in the phrases list a ranking or placement in the ranking.For each of the phrases from the phrases 520 and/or phrases list, thetrending engine may determine a change in the ranking or the placementof the phrase from a previous instance of producing output 540 by thetrending engine. In the output, for each of the phrases from the phrases520 and/or phrases list, the trending engine may determine a change inthe ranking or the placement of the phrase during a predetermined timeperiod. For each of the phrases from the phrases 520 and/or phraseslist, the trending engine may determine a percentage or degree change inthe ranking or the placement of the phrase from a previous instance ofproducing output 540 by the trending engine. For each of the phrasesfrom the phrases 520 and/or phrases list, the trending engine maydetermine a percentage or degree change in the ranking or the placementof the phrase over a predetermined time period.

Referring now to FIG. 5B, an embodiment of a method for identifyingtrends in phrases based on an aggregate of users interactions with anaggregate of content is depicted. In brief overview, the method, at step555, a server, such as via linking system 120, receives a click stream.At step 560, the server identifies phrases from content of decoded linksfrom the click stream. The server may also identify phrases from contentof a predetermined list of web-sites. At step 565, the trending enginedetermined trending phrases, such as based on click velocity. At step570, the trending engine generates a set of trending phases, such asresponsive to determined click velocity.

At step 555, a server or system, such as linking system 120, receivesone or more click streams. The server may receive user actions via clickstreams 250′. The server may a click stream via or comprising a userinteracting, user or accessing a digital resource. The server mayreceive a click stream from the digital resource. The server may receivea click stream via a user requesting to encode an URL. The server mayreceive a click stream via a user clicking on an encoded URL. The servermay receive a click stream via a request to decode an encoded URL. Theserver may receive a clicks stream via a client linking system API orapplication 225. The server may receive a click stream via a log file oractivity log from an application, system, device or server. The servermay decode any encoded URLs of the received click streams to identifyassociated content or content of the URL. The server may decode anyencoded URLs upon receipt of the click stream.

At step 560, the server or system, such as via the keyword extractor,identifies phrases from, associated with, describing or related tocontent that users are interacting with or clicking on. The keywordextractor may identify phrases corresponding to a list of phrases 522from content identified via decoding of encoded URLs. The keywordextractor may operate responsive to receipt of a user action or a clickstream. The keyword extractor may identify phrases corresponding to alist of phrases 522 from content of a predetermined set or list ofweb-sites or URLs. The keyword extractor may identify phrasescorresponding to a list of phrases 522 from content of a predeterminedset or list of digital resources. The keyword extractor may operateresponsive to a predetermined schedule for extracting or identifyingkeywords from these web-sites, URLs or digital resources. The keywordextractor may operate responsive to a change in the list of web-sites,URLs or digital resources. The keyword extractor may filter the phraselist according to ranking, priority, weighting, geography or temporalinformation. The keyword extractor may filter the list of phrases basedon a threshold.

At step 565, the trending engine determines trends in the phrases, suchas the phrases provided or generated by the keyword extractor. Thetrending engine may determine trends responsive to receipt of phrasesfrom the keyword extractor. The trending engine may determine trendsresponsive to a predetermined schedule. The trending engine maydetermine trends on demand, such as responsive to a user request. Thetrending engine may determine trends responsive to any combination ofthe distribution scorer, engagement scorer and time decay functions. Thetrending engine may determine trending phrases from content with thehighest or higher scores from the distribution scorer, engagement scorerand/or time decay functions. The trending engine may determine thenumber of instances of user action with a digital resource associatedwith or comprising one or more phrases. The trending engine maydetermine the number of instances over a predetermined time period ofuser action with a digital resource associated with or comprising one ormore phrases. The trending engine, such as via the velocity engine, maydetermine a velocity of interaction by users with digital resources,such as content, associated with, related to or containing the phrases.The trending engine, such as via the velocity engine, may determine avelocity of interaction by users with content associated with, relatedto or containing the phrases. The trending engine may determine avelocity of user actions on with content associated with, related to orcontaining the phrases. The trending engine may determine a velocity ofclick actions to content associated with, related to or containing thephrases.

At step 570, the trending engine produces or generates a set of trendingphrases responsive to determining the trends in the phrases. Thetrending engine may output the set of trending phrases responsive to thedetermination(s) of step 565. The trending engine may output the set oftrending phrases responsive to a predetermined schedule. The trendingengine may output the set of trending phrases on demand, such asresponsive to a user request. The trending engine may output the set oftrending phrases via an API call, event or function to an application,program or system. The trending engine may output a ranking of trendingphrases, such as top N most upward trending phrases or top N mostdownward trending phrase. The trending engine may output a ranking oftrending phrases, such as top N most popular phrases or top N leastpopular phrases. The trending engine may output an ordered list oftrending phrases in increasing or decreasing velocity. The trendingengine may output an ordered list of trending phrases with greatestchange in velocity. The trending engine may output an ordered list oftrending phrases with slowing or least amount of change in velocity.

Although the systems and methods may be generally described herein inreference to phrases, the systems and methods may be designed andconstructed to determine a trending topic corresponding to a set orgroup of phrases. For example, the trending engine may be designed andconstructed to organize or arrange a group of phrases into a topic. Thetrending engine may be designed and constructed to associate or identifythat a group of phrases correspond to or describe a topic. In someembodiments, the phrases list may be constructed or organized toassociate phrases with topics and the keyword extractor and trendingengine operate responsive to this embodiment of the phrases list. Any ofthe systems and methods described herein may operate or be responsive toa group of phrases and produce a set of trending topics in accordancewith the embodiments described herein.

F. Systems and Methods for Identifying Trends and Relevance of Phrasesfor a User

Referring now to FIGS. 6A and 6B, systems and methods for identifyingphrases that are relevant to specific user from the phrases that aretrending among an aggregate of users interactions with an aggregate ofcontent relevance is depicted. The embodiments of the systems andmethods described in connection with FIGS. 3A and 3B for relevancescoring may be combined with embodiments of the systems and methodsdescribed in connection with FIGS. 5A and 5B for trending to providesystems and methods for identifying trends in phrases across multipleusers and the relevance of such phrases for a particular user. Bycombining trending phrases with relevance scoring, the systems andmethods of embodiments of the present solution can determine whichcontent or phrases is most relevant to a specific user based on trendingphrases from an aggregate set of users.

Referring now to FIG. 6A, a system for identifying those phrasestrending for an aggregate set of users which is relevant to a specificuser is depicted. In brief overview, the system includes a trending andrelevance engine 605 which may include a trending system 505 and arelevance system 300. The system may receive identification of a uservia user id 345. The system identifies via the trending system thosephrases that are trending, such as across an aggregate set of usersinteractions (e.g., clicks) with an aggregate of content. Furtherresponsive to the user id, the system identifies those trending phrasesfrom the aggregate set of user interactions that have the most relevantscore to the user via the relevance system. The system outputs a list ofphrases relevant and trending to the user 610. A content selector 620may use this list to identify the most relevant content to provide orserver the user.

The system may include any embodiments of the trending system 505described in connection with FIGS. 5A and 5B. The system may include anyembodiments of the relevance system 300 described in connection withFIGS. 3A and 3B. The system may include any embodiments of the relevancesearch system 405 described in connection with FIGS. 4A and 4B. Thetrending system may be designed and constructed to interface to,communicate to or integrate with the relevance system. The trendingsystem may provide a list of phrases 540 to the relevance system. Therelevance system may be designed and constructed to interface to,communicate to or integrate with the trending system. The relevancesystem may provide a list of digital resources and relevance scores 330to the trending system. The trending system and relevance system may bedesigned and constructed to work in cooperation or in conjunction witheach other. In some embodiments, the trending system and relevancesystem are combined or constructed into a single application, component,module or system. Any of the above embodiments may be generally referredto as the trending and relevance engine 605 or the relevance andtrending engine 605. The system may apply any of the scoring andweighting functions of any embodiments of the relevance system 300/405described herein in combination with or otherwise to trending phrases.

The trending and relevance engine may comprise any functionality,operations and/or logic to identify phrases that are trending for theuser identified by the user id. The trending and relevance engine maycomprise any functionality, operations and/or logic to identify digitalresources that are most relevant to the user. The trending and relevanceengine may comprise any functionality, operations and/or logic toidentify phrases that are trending upwards and/or downwards in digitalresources interacted with by an aggregate of users and that are most ormore relevant to the user. The trending and relevance engine maycomprise any functionality, operations and/or logic to identify phrasesthat are trending upwards and/or downwards in digital resourcesinteracted with by an aggregate of users and that are least or lessrelevant to the user. The trending and relevance engine may identifyphrases that are trending in digital resources that the user isinteracting with. The trending and relevance engine may identify phrasesthat are trending (upwards or downwards) in digital resources interactedwith by an aggregate of users and having a relevance score for the usergreater than a predetermined threshold. The trending and relevanceengine may identify phrases that are trending (upwards or downwards) indigital resources interacted with by an aggregate of users and having arelevance score for the user less than a predetermined threshold. Thetrending and relevance engine may identify phrases that are trending(upwards or downwards) in digital resources interacted with by anaggregate of users and having a relevance score for the user andvelocity greater than a predetermined threshold.

The trending and relevance engine may generate or calculate a relevancescore for content that the user interacted with and for which includesone or more phrases corresponding to the list of phrases 522. In someembodiments, while the trending and relevance engine determines trendingphrases in content for which users have interacted with, the trendingand relevance engine may also determine a relevance score of howrelevant that content is to the user. In some embodiments, the trendingportion of the trending and relevance engine identifies the trendingphrases content corresponding to the trending phrases. The relevanceportion of the trending and relevance engine may provide a relevancescore for the trending phrase or the content corresponding to each ofthe trending phrases. In some embodiments, the relevance portion of thetrending and relevance engine identifies from the trending phrase orcorresponding content, those trending phrases or content most relevantto the user. In some embodiments, the trending and relevance engineperforms relevance scoring on each content of a plurality of contentcorresponding to a phrase and takes an average, weighted average orother function of these scores to provide a relevance score for allcontent associated with a phrase.

The trending and relevance engine may identify trending or temporallypopular phrases based on aggregating multiple users' interactions withan aggregate of content and comparing such phrases to a list of phrasesof a particular user via the user's click history and/or user profile.The trending and relevance engine may match phrases between theaggregate user's trending phrases and the phrases from the user'shistory or profile to provide a set of trending phrases for the user. Insome embodiments, the match of phrases from the trending phrases of theaggregate users to the phrases of the user may be referred to astrending phrases of or for the user. The trending and relevance enginemay perform a relevance score for such matching or trending phrases ofthe user, or for any content associated with such phrases.

As a result of operation, the trending and relevance engine identifiesphrases that are trending (upwards and/or downwards) in content that anaggregate of users is interacting with/clicking on and which is most ormore relevant (and/or less or least relevant) to the user based on therelevance score. The relevance and trending engine may generate, output,communicate or otherwise provide a list or set of one or more userspecific relevant trending phrases 610. The output 610 may be anenumerated list or ordered list. The output may be a report. The outputmay be a file. The output may be data stored in a database. The outputmay be a web page comprising the user relevant trending phrases. Theoutput may be any digital resource comprising or identifying the userrelevant trending phrases. The trending engine may output the set ofrelevant trending phrases via an API call, event or function to anapplication, program or system. For example, the trending and relevanceengine may output the set of user relevant trending phrases via XML. Thetrending engine may output the set of relevant trending phrases via aweb service call or response to a web service call. The trending andrelevance engine may output the set of user relevant rending phrases viaraising an event or calling a function.

The output 610 may be an encoded URL identifying a digital resourcecomprising or identifying the user relevant trending phrases. In someembodiments, the user relevant trending phrases or output 610 comprisesa list of phrases that are trending upwards in content most relevant tothe user. In some embodiments, the user relevant trending phrases oroutput 610 comprises a list of phrases that are trending upwards incontent least relevant or becoming less relevant to the user. In someembodiments, the user relevant trending phrases or output 610 comprisesa list of phrases that are trending upwards above, below or within apredetermined threshold are most relevant and/or least relevant to theuser. In some embodiments, the user relevant trending phrases or output610 comprises a list of phrases that are trending downwards and are mostand/or least relevant to the user. In some embodiments, the userrelevant trending phrases or output 610 comprises a list of phrases thatare trending downwards in content becoming less relevant or leastrelevant to the user. In some embodiments, the user relevant trendingphrases or output 610 comprises a list of phrases that are trendingdownwards above, below or within a predetermined threshold in contentmost and/or least relevant to the user.

In the output 610, the relevance and trending engine may identify foreach of the phrases for the user a ranking or placement in the ranking.The relevance and trending engine may determine a change in the rankingor the placement of the phrase from a previous instance of producingoutput 610 by the relevance and trending engine. In the output, therelevance and trending engine may identify a change in the ranking orthe placement of the phrase during a predetermined time period. Therelevance and trending engine may determine a percentage or degreechange in the ranking or the placement of the phrase from a previousinstance of producing output 540 by the relevance and trending engine.In the output, the relevance and trending engine may identify apercentage or degree change in the ranking or the placement of thephrase over a predetermined time period. In the output, the relevanceand trending engine may identify a relevance score for each of thetrending phrases. In the output, the relevance and trending engine mayidentify a relevance score and a trending indicator for each of thetrending phrases for the user.

A content selector 620 may select or identify content to serve a userbased on the output 610 from the trending and relevance engine 605. Thecontent selector 620 may comprise an application, program, library,process, service, script, task or any type and form of executableinstructions. The content selector may operate responsive to trendingand relevance engine. The content selector may select content to servera user based on or using any information provided in any embodiments ofthe list of user relevant trending phrases 610. In some embodiments, thecontent selector 620 operates or executes on a system, server, orapplication in communication over a network to the trending andrelevance engine. In some embodiments, the content selector may beembedded or included in a web page or other content served by thesystem, server or application. A content selector may identify from aplurality of content from one or more web-sites the user is visiting,the content that has highest trending phrases and is most relevant touser. Responsive to identifying such content, the system, server, orapplication may serve or provide the content to the user.

Referring now to FIG. 6A, an embodiment of a method for identifyingtrending or temporally popular phrases based on aggregating multipleusers' interactions with an aggregate of content and that are relevantto a specific user is depicted. In brief overview, at step 655, thesystem receives identification of the user. At step 660, the systemdetermines trending phrases for the user. At step 665, the systemdetermines relevance score of content associated with or comprising thetrending phrases. At step 670, the system identifies user relevanttrending phrases. At step 675, content to serve the user is selectedbased on the user relevant trending phrases.

In further details, at step 655, the trending and relevance enginereceives identification of a user via any type and form of user id. Thetrending and relevance engine may receive a request to provide a list ofuser relevant trending phrases for a user identified by the useridentifier. In some embodiments, the trending and relevance enginereceives the user id via a cookie. The trending and relevance engine mayreceive a user identifier or a request from any application, system orserver. In some embodiments, a third party web site serving content maytransmit the request and a user identifier to the trending and relevanceengine. In some embodiments, an ad server serving advertisement ormatching content to impression opportunities may transmit the requestand/or a user identifier to the trending and relevance engine scorer. Insome embodiments, a client application may transmit the request and/or auser identifier to the trending and relevance engine.

At step 660, the trending and relevance engine determines phrases thatare trending based on the aggregate of multiple users' interactions withan aggregate of content and which are relevant, such based on arelevance score, to the user identified by the user id. The trending andrelevance engine may determine trending phrases based on any embodimentsof the systems and methods described in connection with FIGS. 5A and 5B.For example, the trending and relevance engine may determine whichphrases are trending for the aggregate of users and compare thosephrases with a click history or user profile of the user specified bythe user id. The trending and relevance engine may perform or provide arelevance score for those matching phrases.

In another example, the trending and relevance engine may determinetrending phrases for the user based on the number of instances of useraction by the user with a digital resource associated with, related toor containing one or more phrases. The trending and relevance engine maydetermine trending phrases by the user based on the number of instancesover a predetermined time period of user action by the user with adigital resource associated with, related to or containing one or morephrases. The trending and relevance engine, such as via the velocityengine, may determine trending phrases for the user based on a velocityof interaction by the user with digital resources, such as content,associated with, related to or containing the phrases. The trending andrelevance engine, such as via the velocity engine, may determinetrending phrases for the user based on a velocity of interaction by theuser with content associated with, related to or containing the phrases.The trending and relevance engine may determine trending phrases for theuser based on a velocity of user actions by the user with contentassociated with, related to or containing the phrases. The trending andrelevance engine may determine trending phrases for the user based on avelocity of click actions by the user to content containing, related toor otherwise associated with the phrases.

At step 665, the trending and relevance engine determines for the useridentified by the user id the relevance of the phrases from the trendingphrases. In some embodiments, step 665 is performed in conjunction with,during or as part of step 660. For the content containing, related to orassociated with the trending phrases, the trending and relevance enginedetermines a relevance score for the user for such phrases or content.In some embodiments, the trending and relevance engine determines arelevance score for phrases or content associated with phrases trendingupwards for the user. In some embodiments, the trending and relevanceengine determines a relevance score for phrases or content trendingdownwards for the user. In some embodiments, the trending and relevanceengine determines a relevance score for phrases or content associatedwith phrases trending upwards and/or downwards for the user within apredetermined threshold. In some embodiments, the trending and relevanceengine determines a relevance score for phrases or content associatedwith phrases trending upwards and/or downwards for the user within apredetermined time period. In some embodiments, the trending andrelevance engine determines a relevance score for phrases or contentassociated with phrases trending upwards and/or downwards for the userwith or within a predetermined velocity. In some embodiments, thetrending and relevance engine determines a relevance score for phrasesor content associated with a top number of trending phrases.

At step 670, the trending and relevance engine generates or provides alist of user relevant trending phrases 610. The trending and relevanceengine may generate or provide a list of user relevant trending phrases610 responsive to steps 655, 660 and/or 665. The trending and relevanceengine may enumerate the list of user relevant trending phrases 610 inascending or descending order. The trending and relevance engine mayenumerate the list of user relevant trending phrases 610 based onrelevance. The trending and relevance engine may enumerate the list ofuser relevant trending phrases 610 based on trend velocity. The trendingand relevance engine may enumerate the list of user relevant trendingphrases 610 based on relevance and trend velocity. The trending andrelevance engine may enumerate the list of user relevant trendingphrases 610 based on a function of relevance and trend velocity. Thetrending and relevance engine communicates or provides a list of userrelevant trending phrases 610 to a requestor, such as a user,application, server or system. The trending and relevance enginecommunicates or provides a list of user relevant trending phrases 610 tothe content selector.

At 675, a content selector may select content to serve the userresponsive to the trending and relevance engine. The content selectormay select content from a plurality of possible content to serve theuser based on the user relevant trending phrases. The content selectormay select content from a plurality of possible content to serve theuser based on the highest trending phrase in the user relevant trendingphrases. The content selector may select content from a plurality ofpossible content to serve the user based on the most relevant phrase inthe user relevant trending phrases. The content selector may selectcontent from a plurality of possible content to serve the user based onthe most relevant and highest trending phrase in the user relevanttrending phrases. In some embodiments, the content selector provides adigital resource id and user id to the trending and relevance engine todetermine what content to select and serve to the user.

G. Systems and Methods for Tracking Influence of a User on ContentShared Via Encoded Uniform Resource Locator (URL) Link

Referring now to FIGS. 7A and 7B, systems and methods for trackinginfluence of a user on content shared via encoded URLs is depicted.Influence of a user, generally referred to as influence, may identifywhat level of engagement by other users does a particular user drive tocontent when the user shares content. For example, a high influencer maybe a user who drives a high level of engagement with content when theuser shares content. A low influencer may be a user who does not drivesa high level of engagement, or otherwise drivers a low level ofengagement with content when the user shares content. To measure or rateinfluence, embodiments of the systems and methods of the presentsolution may correlate who encoded a link, how important or how muchtraffic does the encoded link generate, who clicked on the encoded linkand the topics or phrases associated with the links. From thiscorrelation, these systems and methods may determines a ranking orrating of the influence of a user which may stored in the user'sprofile. The influence rating of the user may be used by a relevancesystem to provide user weighting to relevance and trending scoringsystems, such as any embodiments of such systems herein.

Referring now to FIG. 7A, a system for tracking influence of a user isdepicted. In brief overview, the system may include a keyword extractor515, user tracker 215 and click tracker 220 as previously describedherein. The system may include an influence tracker 720 that correlatesinformation from the keyword extractor 515, user tracker 215 and clicktracker 220 to determine an influence rating 712A-712N (generallyreferred to as influence rating 712) for each of a plurality of users,which may be stored in the user's profile 710A-710N (generally referredto as user profile 710). Based on the influence rating of a user, arelevance system 605 may provide user weighting 430 to a relevance scoreto up weight or down weight the score to result in a user influencerelevance score 725. A user's influence rating 712 may be queried orobtained by request using a user identifier 345.

The trending and relevance system 605 may comprise any embodiments ofthe trending and relevance system 605 described herein. In someembodiments, the trending and relevance system 605 comprises thetrending system 405. In some embodiments, the trending and relevancesystem 605 comprises the trending system 505. In some embodiments, thetrending and relevance system 605 comprises the relevance system 300. Insome embodiments, the trending and relevance system 605 comprises therelevance system 300. In some embodiments, the trending and relevancesystem 605 comprises the relevance system 405. Any of these embodimentsmay be referred to as a relevance based system.

Each of the keyword extractor 515, user tracker 215 and click tracker220 may comprise any embodiments of keyword extractor 515, user tracker215 and click tracker 220 described herein. The keyword extractor, usertracker and click tracker may be designed and constructed to interfaceto, communicate with or integrate to the influence tracker. Theinfluence tracker may be designed and constructed to interface to,communicate with or integrate to keyword extractor, user tracker and/orclick tracker. The keyword extractor, user tracker and click tracker maybe designed and constructed to work in cooperation or in conjunctionwith the influence tracker. In some embodiments, the keyword extractor,user tracker, click tracker and influence tracker 720 are combined orconstructed into a single application, component, module or system. Anyof the above embodiments may be generally referred to as an influencesystem or the influence tracker 720.

In further details, the influence tracker 720 may comprise anapplication, program, library, process, service, script, task or anytype and form of executable instructions. The influence tracker maycomprise functions, operations and logic to determine the influence of auser based on information tracked by the linking system relative to,associated with or in connection with a specific user. In someembodiments, the influence tracker may operate responsive to the linkencoder and/or link decoder. In some embodiments, the influence trackermay operate responsive to the user tracker. In some embodiments, theinfluence tracker may operate responsive to the click tracker and/orclick analyzer 235.

The influence tracker may identify, track and correlate information onwhat links the user encoded, the traffic generated by the user's encodedlinks, who clicked on the user's encoded links and topics or phrasesassociated with the content from the user's encoded links. In someembodiments, the influence tracker identifies and tracks the URLsencoded by a user. In some embodiments, the influence tracker identifiesand tracks the number of URLs encoded by a user, such as via the linkencoder and user tracker. In some embodiments, the influence trackeridentifies and tracks the content from the URL encoded by the user. Insome embodiments, the influence tracker identifies and tracks keywordsand/or phrases of the content from the URL encoded by the user. In someembodiments, the influence tracker identifies and tracks topics orsubject matter of the content from the URL encoded by the user. In someembodiments, the influence tracker identifies and tracks trendingphrases of the content from the URL encoded by the user. In someembodiments, the influence tracker identifies and tracks the number ofclicks and/or number of other users who clicked on the encoded URLencoded by the user. In some embodiments, the influence trackeridentifies and tracks the sources or sites from which of other usersclicked on the encoded URL encoded by the user.

The influence tracker may store any of the identified and trackedinformation associated with the user to a user profile 710. The userprofile may comprise a data structure, data object, file or one or moretables, such as data, objects or tables stored in a database. The userprofile may store an aggregation of the information identified andtracked by the influence tracker. The user profile may store anystatistics or metrics of the information identified and tracked by theinfluence tracker. The user profile may store a history of any theinformation identified and tracked by the influence tracker. The userprofile may store influence rating of the user. The user profile maystore a history of influence ratings of the user, such as changes andupdated to the influence rating of the user.

The influence tracker may process, analyze and correlate any of theinformation tracked by the system of the user, such as any informationtracked via the above described embodiments or stored in the userprofile, to determine, generate or otherwise provide an influence ratingor score 712 for the user. The influence rating or score of a useridentifies a level of engagement by other users that a particular userdrives or causes when sharing content (e.g. how much influence as userhas for others to interact or engage with content when the user sharescontent). An influential user is someone who drives or causes a higheror high level of engagement or interaction with content when the usershares content such as via forwarding or distributing encoded links. Theinfluence rating may identify a number of clicks from other users that aparticular user drives when sharing content. In some embodiments, theinfluence tracker generates the influence rating 712 based on applying afunction or algorithm to any combination of number of encoded URLsencoded by the user, how much traffic (e.g., number of clicks) generatedby encoded URLs encoded by the user, who clicked on the encoded URLsencoded by the user and the topics associated with the encoded URLsencoded by the user. In some embodiments, the influence tracker ingenerating or computing the influence rating may weight any of thecomponents or factors making up the influence rating in any manner. Insome embodiments, the influence tracker uses a time decay function 435to modify, change or affect the influence rating based on temporalinformation related to user and any of the components or factors makingup the influence rating.

For example, in some embodiments, the influence tracker may compute theinfluence rating as function of a number of URLs encoded by the user andthe number of clicks on the encoded URLs encoded by the user. In anotherexample, the influence tracker may compute the influence rating asfunction of a number of URLs encoded by the user and/or the number ofclicks on the encoded URLs encoded by the user and the influence ratingof users who clicked on the encoded URL of the user. In another example,the influence tracker may compute the influence rating as function of anumber of URLs encoded by the user and/or the number of clicks on theencoded URLs encoded by the user and the popularity or ranking of theweb-sites from which users clicked on the encoded URL of the user. Inanother example, the influence tracker may compute the influence ratingas function of a number of URLs encoded by the user and/or the number ofclicks on the encoded URLs encoded by the user and the popularity orranking of the phrases, keywords or topics of the content from orassociated with the encoded URL of the user. In another example, theinfluence tracker may compute the influence rating as function of anumber of URLs encoded by the user and/or the number of clicks on theencoded URLs encoded by the user and the trending of phrases, keywordsor topics of the content from or associated with the encoded URL of theuser. In another example, the influence tracker may compute theinfluence rating as a function of a relevance score for content ofencoded URLs of the user. In another example, the influence tracker maycompute the influence rating as a function of an engagement score and/ordistribution score for content of encoded URLs of the user. In anotherexample, the influence tracker may compute the influence rating as afunction of geography of the user and/or the users clicking on theuser's encoded URL.

In another example, the influence tracker may compute the influencerating as a function of any combination of a relevance score, engagementscore, distribution score, social score, geography, search relevancescore, a number of URLs encoded by the user and/or the number of clickson the encoded URLs encoded by the user, popularity or ranking ofweb-sites from which users clicked the encoded URL of the user,popularity or ranking of the phrases, keywords or topics of the contentfrom or associated with the encoded URL of the user and/or the trendingof phrases, keywords or topics of the content from or associated withthe encoded URL of the user

The influence rating 712 may comprise a value that provides anindication of or otherwise identifies how influential a user, such asthe influence of a user when sharing encoded URLs. The influence ratingmay be generated for or on an absolute or relative scale. The influencerating may be generated for or normalized to a predetermined influencerating range, such as for example −100 to 100, 0 to 100 or X to Y. Theinfluence tracker may store the influence rating to the user's profile.The influence tracker may update the influence rating in the user'sprofile.

As click streams are received over time by the system 120, the influencetracker may regenerate, re-compute or re-determine the influence ratingof a user. In some embodiments, the influence tracker determines theinfluence rating dynamically in real-time. As a click stream is receivedthat impacts or affects the user's influence, the influence tracker mayupdate the user's profile with tracked information and regenerate theuser's influence rating. In some embodiments, the influence trackerdetermines the influence rating on a predetermined basis, such as once aday at a certain time. In some embodiments, the influence trackerdetermines the influence rating on an adhoc or on-demand basis, such asresponsive to a request for the user's influence rating. In some theinfluence tracker determines the influence rating on an event basis,such as when the user encodes a URL or the system receives a user actionfrom a click of the encoded URL encoded by the particular user.

The influence rating 712 and/or user profile may provide for or impactthe user weighting 430 applied during any trending and/or relevancescoring system 605 described herein. In some embodiments, the influencerating is the user weighting. In some embodiments, the user weighting isa function of the influence rating. In some embodiments, the system 605may convert the influence rating using any type and form of scaling orconversion factor to the user weighting 430. In some embodiments, thesystem 605 queries the influence tracker for the influence rating of auser identified by the user id 345. In some embodiments, the system 120uses the influence rating 712 to up weight or down weight any relevancescore and/or trending indicator. In some embodiments, the system 120uses information in user profile(s) to up weight or down weight anyrelevance score and/or trending indicator.

Based on applying the influence rating and/or user weighting and/or theuser profile, the system 605 provides a user influenced relevance score725. In some embodiments, the relevance scores 325 described in FIG. 3Amay use the influence rating 712 and/or user weighting 430 to determineor generate a relevance score 330 that is a user influenced relevancescore 725. In some embodiments, the user influenced relevance score maycomprises the relevance score(s) for content in the relevance searchresults 450 as described in FIG. 4A with the user weighting 430 based onthe influence rating 712 of a specified user. In some embodiments, thetrending/ranked phrases 540 described in FIG. 5A may use the influencerating 712 and/or user weighting 430 to determine or generate a userinfluenced relevance score 725 for such trending/ranked phrases. In someembodiments, the relevant trending phrases 610 described in FIG. 6A mayuse the influence rating 712 and/or user weighting 430 to determine orgenerate a user influenced relevance score 725 for such relevanttrending phrases 610.

Referring now to FIG. 7B, a method for tracking influence of a user isdepicted. In brief overview of the method at step 755, the systemreceives identification of the user for each encoded URL. At step 760,the system identifies keywords, phrases and topics from contentassociated with each encoded URL. At step 765, the system determines anumber actions and other metrics via user uses the decoded each encodedURL of the user. At step 770, the system generates an influence ratingand/or user weighting. At step 775, the system may up weight or downweight a relevance score based on a profile of the user, such as theuser's influence rating.

In further details, at step 755, the system identifies the user encodingthe URL. In some embodiments, the system identifies the user via a userid 345. In some embodiments, the system indentifies the user via anaccount or login of the linking system 120. In some embodiments, thesystem identifies the user via a cookie 255. The system such as via thelink encoder and user tracker may track each instance of the userencoding a link and store that information to the database 230 and/or tothe user's profile 710. The system may track when the user encoded thelink, what link was encoded and identification of the encoded link andstore this information to the user's user profile.

At step 760, the system may identify keywords, phrases and/or topicsfrom the content associated with, identified by or included in the URLor link encoded by the user. Each time the user encoded a link/URL, thesystem may identify keywords, phrases and topics for the encoded URLusing any embodiments of the content extractor 315 and/or keywordextractor 515 described herein. In some embodiments, the contentextractor 315 and/or keyword extractor may identify keywords, phrasesand topics for content of or associated with the encoded URL. In someembodiments, the content extractor 315 and/or keyword extractor mayidentify keywords, phrases and topics upon the request to encode the URLor upon encoding the URL. The system may track when the keywords,phrases and/or topics of content of or from URLs that the user requeststo encode and stores this information to the user user's user profile.The system may store this information in correlation with or associationwith the when the user encoded the link, what link was encoded andidentification of the encoded link stored as part of step 760.

At step 765, the system may receive a plurality of click streams thatidentify user actions 250 with the encoded URL encoded by the user. Thesystem, such as via the click tracker, may identify in the click streamsrequests to decode the encoded URL encoded by the user. The system, suchas via the link decoder, may identify and track each time the encodedURL of the user is decoded. The system, such as via the click tracker,may identify the source of the user action to decode the encoded URL.For example, the system may identify the source IP address of thenetwork traffic carrying the user action to decode the encoded URL. Thesystem may identify the web-site, such as the social networking site,from which the network traffic carrying the user action to decode theencoded URL originated. The system may identify the web-site, such asthe social networking site, from which user clicked on the encoded URL.The system may track the number of times the encoded URL of the user hasbeen clicked. The system may track the number of different users thathave clicked the encoded URL of the user. The system may track thenumber of different sources (e.g., web-sites) from which the encoded URLof the user was clicked. The system may track the number of differentgeographic locations from which the encoded URL of the user was clicked.The system may compute any metrics on the information such as averages,peaks, trends, minimums, maximums, etc. The system may store any of theidentified or tracked information and any metrics thereof in the user'suser profile. The system may store any of the identified or trackedinformation and any metrics thereof in the user's user profile incorrelation with or association with any of the information stored inthe user's user profile via steps 760 and/or 765.

At step 770, the influence tracker may determine, generate or otherwiseprovide an influence rating for the user based on the informationidentified and tracked via steps 755, 760 and/or 765. The influencetracker may use the information stored in the user's profile to generatethe influence rating. The influence tracker may receive the informationfrom any one or more of the keyword extractor, click tracker and usertracker to generate the influence rating. The influence tracker maygenerate the influence rating for the user as each click stream for anencoded URL of the user is received and analyzed. The influence trackermay generate the influence rating for any one or more users on apredetermined frequency, such as once per day. The influence tracker maygenerate the influence rating for a user on a per demand basis, such asupon receiving a request for the influence rating of a user specified bya user id. The influence tracker may store the generated influencerating in the user's user profile and update the influenced rating eachtime the influence tracker regenerates the influence rating.

At step 775, the influence rating of the user may influence or affectany relevance scores generated by any embodiments of the systemsdescribed herein. In some embodiments, a relevance based system queriesthe influence tracker to determine the influence rating of a user, suchas via a request and response mechanism. In some embodiments, arelevance based system queries the user profile of a user to determinethe influence rating of the user. In some embodiments, the relevancebased system uses or applies the influence rating as a user weighting indetermining a relevance score. In some embodiments, the relevance basedsystem coverts or transforms the influence rating to a user weighting indetermining a relevance score. In some embodiments, the relevance basedsystem uses the influence rating to up weight or down weight a relevancescore.

H. Systems and Methods for Providing a Recommended List of URLsResponsive to a URL

Referring now to FIGS. 8A and 8B, systems and methods of embodiments ofthe present solution for providing a recommendation list of URLs orlinks given an input or URL link is depicted. Given an input of a linkor URL (e.g., an input link), the present solution may provide an outputof a list of recommended content in ranked order. In some embodiments,the present solution performs these systems and methods without anypersonal or other information about the user—the only input is a URL andthe output is a list of URLs and in some embodiments, also a matching orrecommendation score. The present solution maintains a clickco-occurrence map, sometimes also referred to as click coherency map, tocorrelate all the users that clicked on the input link to the otherlinks which have been clicked on by the users who also clicked on theinput link. The click co-occurrence map may identify the largest numberof those other links to which the users clicked and also clicked on theinput link. The output may be filtered to be domain or content-sitespecific. In other embodiments, raw or unfiltered output may be usefulto aggregators who may provide recommended content from a wider ser ofcontent resources.

Referring now to FIG. 8A, an embodiment of a system for providing arecommendation list of URLs or links given an input or URL link isdepicted. In brief overview, the system may include a user tracker 215,click tracker 220 and recommendation system 805. The system may alsoinclude a correlation engine 820 that maintains a click co-occurrencemap 825 from click streams 250. The correlation engine may correlate viathe click co-occurrence map users that clicked on an encoded URL 805 toother encoded URLS those users clicked on 810. The user tracker 215 mayidentify those who users clicked on the encoded URL 805 and who alsoclicked on other encoded URLs. The click tracker may identify the numberof clicks on the encoded URL 805 and for each of the other encoded URLsthe users clicked. The correlation engine may store the clickco-occurrence map into a database 230. The recommendation system 805 mayreceive a URL 205 as input and output a set of URLs based on the clickco-occurrence map 825.

The user tracker may comprise any embodiments of the user tracker 215previously described herein. For any click stream 250′ received by thesystem 120, the user tracker may identify the user who encoded theencoded URL and each of the other users who clicked on the encoded URLof the user. The click tracker may comprise any embodiments of the usertracker 215 previously described herein. For any click stream 250′received by the system 120, the user tracker may identify the number ofusers who encoded the same URL. For any click stream 250′ received bythe system 120, the click tracker may identify the number of users whoclicked on the encoded URL of a user. The click tracker may identify foreach user the number of encoded URLs that user clicked.

Via the user tracker and/or click tracker, the system identifies andtracks the users that clicked on an encoded URL 805. For each usertracked or managed by the system, the system may identify the otherencoded URLs 810 that the same users who clicked the encoded URL 805also clicked. The encoded URL 805 and the other encoded URLs 810 may beencoded URLs of any user. The encoded URL 805 and the other encoded URLs810 may be encoded URLs of the system. The encoded URL 805 and the otherencoded URLs 810 may identify or be associated with any content from anysource. For each encoded URL 805, 810 tracked or managed by the system,the system may identify each of the users the clicked on each encodedURL. The system may identify each user via one or cookies. The systemmay identify each user via information sent with requests to decode theencoded URL. The system may identify each user via the user's accountinformation for the system. In some embodiments, the system tracks theuser id of the user who clicked on the encoded URL without trackingother user information. In some embodiments, the system may identify thesource (e.g., web-site, social networking site, device, etc) from whichthe user clicked on the encoded URL, the time and/or geography fromwhich the user clicked on the encoded URL. The system, such as via usertracker and click tracker, may store the users and number of clicks forthe encoded URLs 805, 810 to the database 230.

The correlation engine 820 may comprise an application, program,library, service, script, process, task or any type and form ofexecutable instructions executing or executable on a device. Thecorrelation engine may comprise logic, function or operations tocorrelate any URL to a plurality of other URLs. The correlation enginemay comprise logic, function or operations to correlate an encoded URLto a plurality of other encoded URLs. The correlation engine maycomprise logic, function or operations to correlate the users theclicked on encoded URLs 805 to the encoded URLs 810 that the same usersalso clicked. The correlation engine may correlate URLs/encoded URLs toother URLs/encoded URLs based on the number of clicks. In someembodiments, the correlation engine may correlate URLs/encoded URLs toother URLs/encoded URL also based on the source of the clicks, the timeof the clicks and/or the influence of the users or who performed theclicks.

The correlation engine 820 may be designed and constructed to generateand/or maintain a click co-occurrence map 825, sometimes referred to asa map. The click co-occurrence map may comprise any type and form ofdata structure, object, file, table(s) and/or arrangement of data storedin a database. The click co-occurrence map may identify or specify thecorrelation between URLs/encoded URLs to other URLs/encoded URLs. Theclick co-occurrence map may identify or specify the encoded URLs 810 forwhich the users who clicked on the encoded URL 805 also clicked. Foreach encoded URL 810, the click co-occurrence map may identify or trackthe number of such users who clicked both the encoded URL 805 and eachencoded URL 810. For each of the other encoded URLs, the correlationengine may correlate the users and numbers of clicks on these encodedURLS and further encoded URLs those same users also clicked. In someembodiments, the correlation engine may maintain a click co-occurrencemap for a plurality of depths or levels.

In some embodiments, a click co-occurrence map comprises a table, or anyprogrammatic representation thereof, that identifies along one axis,such as the vertical axis, a list of the content the tracked usersinteracted with and along another axis, such as horizontal axis, eachuser. The table may identify or indicate for each user in the horizontalaxis which content in the list of content in the vertical axis that theuser has interacted with (e.g. clicked on). From such a table, thesystem can identify users who have interacted with the same content(e.g., similar interests). The system can also identify for users withsimilar interests what content one user may have interacted with or haveinterest in that the other user has not yet seen or interacted with. Bythe system performing comparison of similarities and differences betweenusers in the map, the system may make recommendations of content for aparticular user.

For each encoded URL 810, the click co-occurrence map may use any numberof thresholds to determine whether or not the other encoded URLs hasbeen clicked enough times or by enough users to be put or maintainedwithin the map. For each encoded URL 810, the click co-occurrence mapmay use any temporal thresholds to determine whether or not the otherencoded URLs 810 are put or maintained within the map. For each encodedURL 810, the click co-occurrence map may use any user influence or userweighting to determine whether or not the other encoded URLs 810 are putor maintained within the map. For each encoded URL 810, the clickco-occurrence map may use any content relevancy score to determinewhether or not the other encoded URLs 810 are put or maintained withinthe map. For each encoded URL 810, the click co-occurrence map may useany trending score to determine whether or not the other encoded URLs810 are put or maintained within the map.

The correlation engine may generate and/or maintain a plurality of clickco-occurrence maps. The correlation engine may generate and/or maintaina click co-occurrence map corresponding to each encoded URL 805. Thecorrelation engine may generate and/or maintain a click co-occurrencemap for a set of encoded URLs 805. The index to a click co-occurrencemap may be an URL or encoded URL. The correlation engine may generateand/or maintain a single click co-occurrence map for all the encodedURLs.

The recommendation system 805 may comprise an application, program,library, service, script, process, task or any type and form ofexecutable instructions executing or executable on a device. Therecommendation engine may comprise logic, function or operations toprovide a recommendation of one of more other URLs given a URL as input.The recommendation engine may comprise logic, function or operations toanalyze the click co-occurrence map to determine similarities and/ordifference between users in what content users have interacted with orco-clicked and what content users may not have not co-clicked. Therecommendation system 805 may comprise any embodiments of the relevancesystem 405 described herein. The recommendation system may be designedand constructed to receive a URL 205 as input and to produce a set ofone or more URLS 820 as output, such as based on the click co-occurrencemap.

For any output URL, the recommendation system may provide a score, suchas a recommendation or matching score, based on the level of matchingvia the co-occurrence map. The score may indicate or identify the numberof click co-occurrences for the URL in the click co-occurrence map. Therecommendation score may indicate the number of other users with similarclick history or behavior who also clicked on the recommended outputURL. In some embodiments, the recommendation score may be an order orranking of the URL in the list of recommended URLs. In some embodiments,the score may be a relevance score if a user is specified with the URLinput.

The recommendation system may comprise any type and form of interface toreceive a URL 205, such as a graphical user, command line interface orprogrammatic interface. In some embodiments, the programmatic interfaceof the relevance system 405 may comprise an API, web-service orrequest/response mechanism. In some embodiments, the recommendationsystem may receive a plurality of URLs in a single request. In someembodiments, the recommendation system may receive an encoded URL anddecodes the encoded URL.

The recommendation system, such as a responsive to receipt of a URL 205,may be designed and constructed to read, process or query a clickco-occurrence map for or corresponding to the URL. The recommendationsystem may obtain or retrieve via the database a corresponding clickco-occurrence map, such as by using the input URL as an index forretrieval. Via the click co-occurrence map, the recommendation systemmay identify or query the URLs that are mapped to the input URL. Therecommendation system may output the set of mapped URLs 830 via any theinterface, such as displaying via a graphical user interface, output viaa command line interface or via a response of a programmatic interface.The recommendation system may rank the mapped URLs by the number ofclicks on the mapped URLs. The recommendation system may output anenumeration of URLs 830 by ranking.

The recommendation system may filter any of the ranked or unranked URLs830 based on a number of clicks threshold. The recommendation system mayfilter any of the ranked or unranked URLs 830 based on domain filtering.The recommendation system may filter any of the ranked or unranked URLs830 using content based or content specific filtering. In someembodiments, if a user is also specified with the URL input, therecommendation system may also provide a relevance score for each of theURLs 830 by using any of the relevance scoring systems, methods andtechniques previously described herein.

Referring now to FIG. 8B, an embodiment of a method for providing arecommendation list of URLs or links given an input or URL link isdepicted. In brief overview, at step 855, the system, such as anyembodiments of linking system 120, identifies the users who clicked onan encoded URL. At step 860, the system identifies the plurality ofother encoded URLs those users clicked on. At step 865, the systemgenerates a click co-occurrence map, such as to correlate the encodedURL of step 855 to the plurality of other encoded URLs clicked by eachuser at step 860 who also clicked the encoded URL. At step 870, thesystem received a URL as input and at step 875, as output, the systemprovides an enumeration of recommended URLs based on a clickco-occurrence map. In some embodiments, the output may also include aranking and/or recommendation score.

At step 855, the system identifies each of the users who clicked on anencoded URL 805, requested to decode an encoded URL 805 or otherwiseinteracted with the encoded URL or corresponding URL. For each clickstream and/or decoding of the encoded URL, the system may track for eachencoded URL the users who clicked on the encoded URL. The system mayidentify each user via a user id 345. In some embodiments, the systemindentifies the user via an account or login of the linking system 120.In some embodiments, the system identifies the user via a cookie 255.The system such as via the link encoder and user tracker may track eachinstance of the user decoding an encoded link and store that informationto the database 230 and/or to the user's profile 710. The system maytrack when the user decoded the link, what encoded link was decoded andidentification of the link and store this information to the database,such as in the user's user profile.

At step 860, for each of the users identified as clicking on the encodedURL 805, the system identifies the other encoded URLs 810 those usersalso clicked or have clicked. The system via user tracker and clicktracker may store in the database a history of encoded URLs each of theusers have previously clicked on. The system may also continue to track,such as in real-time, the encoded URLs each of the users are clickingon. For each click stream and/or decoding of an encoded URL, the systemmay track by user the encoded URLs that user has clicked on. The systemmay track and store in the user's user profile any informationidentifying the encoded URLs/URLs that user has clicked on or otherwiseinteracted with.

At step 865, the system correlates the user and click trackinginformation, such as the user and click information obtained via steps855 and 860 to generate and/or maintain one or more click co-occurrencemaps. For each encoded URL 805 and for each of the users that clicked onor interacted with each encoded URL 805, the system may identify theother encoded URLs 810 that user also clicked on or interacted with. Foreach encoded URL 810, the click co-occurrence map may identify or trackthe number of such users who clicked both the encoded URL 805 and eachencoded URL 810. The system may maintain a click co-occurrence map tocorrelate each encoded URL/URL 805 to the other encoded URLs/URLs 810.The system may maintain a separate click co-occurrence map for eachencoded URL/URL. The system may maintain a click co-occurrence map forall encoded URL/URLs that is indexed by or organized by encoded URL. Thesystem may update the click co-occurrence map upon processing each clickstream. The system may update the click co-occurrence map upon decodingeach encoded URL.

At step 870, the system receives an input identifying or comprising aURL. The system may receive the input via a request. The system mayreceive the URL as input via a graphical user interface. The system mayreceive the URL as input via a command line interface. The system mayreceive the URL as input via a programmatic interface. For example, thesystem may receive the URL as input via an API call, such as a webservice call. The system may receive the URL as input via an HTTPrequest. The system may receive the URL as input from any component ofthe linking system 120. The system may receive the URL as input from anythird party system, including applications, servers and systems. Thesystem may receive the URL as input from a web page, script or otherexecutable instructions executing on a web page or web server. Thesystem may receive the URL as input from any type and form of mobileapplication executing on a mobile device, such as a smart phone.

The system may also receive with the URL input or otherwise associatedwith a request, any one or more parameters for filtering the output.These parameters may include geography information. These parameters mayinclude language information or identification. These parameters mayinclude identification or specification of domain filters and/or contentfilters. These parameters may include identification or specification ofany thresholds, such as number of recommended URLs. These parameters mayinclude identification or specification of a ranking or ordering of theoutput. These parameters may include identification or specification ofproviding or generating a score for the URLs in output, such as usingany of the relevance scoring techniques described herein.

At step 875, the system provides as output 830 one or more URLs. Thesystem may provide the output responsive to receipt of the URL 805 asinput. The system may use the input URL to retrieve, lookup, query orobtain a click co-occurrence map, such as from database 230, for orcorresponding to the input URL. Via the click co-occurrence map, thesystem may identify a plurality of encoded URLs clicked on by users whoalso clicked on the input URL. Via the click co-occurrence map, thesystem may determine a plurality of encoded URLs clicked on by thelargest number of users who also clicked on the input URL. The systemmay provide the output 830 via a graphical user interface or commandline interface. The system may provide the output 830 as a response to arequest, such as an HTTP response to an HTTP request. The system mayprovide the output 830 as a response or data structure to an API call.The system may provide the output as an enumerated list of URLs. Thesystem may provide the output as an enumerated list of URLs ranked inorder based on number of user interactions/clicked and/or relevance.

The system may apply any filtering, such as domain or content filtering,based on any filtering parameters specified via the input or request.For example, the system may exclude URLs from the output that match orcorrespond to a domain. In another example, the system may exclude URLsfrom the output that link to or comprise content corresponding to orhaving keywords corresponding to specified content. The system may applyany threshold based on any threshold parameters specified via the inputor request. For example, the system may provide as output a top 10ranking of URLs. In another example, the system may provide an output ofURLs that had at least a predetermined number of clicks (e.g., 100,1000, etc). As may be requested via the input or the request orotherwise as a default, the system may determine or provide a score witheach URL 830 in the output. The system may give each URL in the output ascore based on the level or degree of matching or recommendation fromanalysis of the click co-occurrence map. If a user is specified with theinput URL, the system may give each URL in the output a score based onany of the relevance scoring techniques described herein.

It should be understood that any of the systems described above mayprovide multiple ones of any or each of those components and thesecomponents may be provided on either a standalone machine or, in someembodiments, on multiple machines in a distributed system. The systemsand methods described above may be implemented as a method, apparatus orarticle of manufacture using programming and/or engineering techniquesto produce software, firmware, hardware, or any combination thereof. Inaddition, the systems and methods described above may be provided as oneor more computer-readable programs embodied on or in one or morearticles of manufacture. The term “article of manufacture” as usedherein is intended to encompass code or logic accessible from andembedded in one or more computer-readable devices, firmware,programmable logic, memory devices (e.g., EEPROMs, ROMs, PROMs, RAMs,SRAMs, etc.), hardware (e.g., integrated circuit chip, FieldProgrammable Gate Array (FPGA), Application Specific Integrated Circuit(ASIC), etc.), electronic devices, a computer readable non-volatilestorage unit (e.g., CD-ROM, floppy disk, hard disk drive, etc.). Thearticle of manufacture may be accessible from a file server providingaccess to the computer-readable programs via a network transmissionline, wireless transmission media, signals propagating through space,radio waves, infrared signals, etc. The article of manufacture may be aflash memory card or a magnetic tape. The article of manufactureincludes hardware logic as well as software or programmable codeembedded in a computer readable medium that is executed by a processor.In general, the computer-readable programs may be implemented in anyprogramming language, such as LISP, PERL, C, C++, C#, PROLOG, or in anybyte code language such as JAVA or in any script language, such asPython or TCL. The software programs may be stored on or in one or morearticles of manufacture as object code.

While various embodiments of the methods and systems have beendescribed, these embodiments are exemplary and in no way limit the scopeof the described methods or systems. Those having skill in the relevantart can effect changes to form and details of the described methods andsystems without departing from the broadest scope of the describedmethods and systems. Thus, the scope of the methods and systemsdescribed herein should not be limited by any of the exemplaryembodiments and should be defined in accordance with the accompanyingclaims and their equivalents.

What is claimed:
 1. A method for identifying trends in phrases ofcontent based on user interaction with the content, the methodcomprising: (a) receiving, by a server of a linking system,identification of a plurality of clicks of encoded uniform resourcelocator (URL) links, each of the encoded URL links encoded by the serverof the linking system and linked to a URL corresponding to a destinationserver; (b) identifying, by the server, for each of the plurality ofclicks, in content identified from decoding the encoded URL links, aplurality of phrases that correspond to a predetermined set of keywords;(c) determining, by the server, a velocity of clicks on contentcorresponding to each phrase of the plurality of phrases; (d)identifying, by the server, a trend in one or more phrases of theplurality of phrases based on the velocity of clicks.
 2. The method ofclaim 1, wherein step (a) further comprises receiving, by the server,identification of the plurality of clicks from a plurality of differentusers via a plurality of different sources.
 3. The method of claim 1,wherein step (b) further comprises decoding, by the server, each of theencoded URL links to obtain a URL to the content.
 4. The method of claim1, wherein step (b) further comprises identifying the plurality ofphrases in the content based on one of text or meta-data in the content.5. The method of claim 1, wherein step (b) further comprises identifyingone or phrases of the plurality of phrases in the content that deviatesfrom a predetermined norm for the content.
 6. The method of claim 1,wherein step (c) further comprises determining the velocity, by theserver, based on a number of clicks via one or more encoded URL links ofthe encoded URL links within a predetermined time period on contentcorresponding to a phrase.
 7. The method of claim 1, wherein step (c)further comprises determining velocity, by the server, based on a rateof clicks via one or more encoded URL links of the encoded URL links tocontent corresponding to a phrase.
 8. The method of claim 1, whereinstep (c) further comprises determining velocity, by the server, based ona change in rate of clicks via one or more encoded URL links of theencoded URL links to content corresponding to a phrase.
 9. The method ofclaim 1, wherein step (d) further comprises enumerating, by the server,a list of phrases from the plurality of phrases based on an increasingvelocity of clicks.
 10. The method of claim 1, wherein step (d) furthercomprises enumerating, by the server, a list of phrases from theplurality of phrases based on a decreasing velocity of clicks.
 11. Asystem for identifying trends in phrases of content based on userinteraction with the content, the system comprising: a server receivingidentification of a plurality of clicks of encoded uniform resourcelocator (URL) links, each of the encoded URL links encoded by the serverof the linking system and linked to a URL corresponding to a destinationserver, the server comprising a hardware processor; a content extractor,executing on the processor the server, identifying, for each of theplurality of clicks, in content identified from decoding the encoded URLlinks, a plurality of phrases that correspond to a predetermined set ofkeywords; and a trending engine, executing on the processor of theserver, determining a velocity of clicks on content corresponding toeach phrase of the plurality of phrases and identifying a trend in oneor more phrases of the plurality of phrases based on the velocity ofclicks.
 12. The system of claim 11, wherein the server receivesidentification of the plurality of clicks from a plurality of differentusers via a plurality of different sources.
 13. The system of claim 11,wherein the server decodes each of the encoded URL links to obtain a URLto the content.
 14. The system of claim 11, wherein the contentextractor identifies the plurality of phrases in the content based onone of text or meta-data in the content.
 15. The system of claim 11,wherein the content extractor identifies one or phrases of the pluralityof phrases in the content that deviates from a predetermined norm forthe content.
 16. The system of claim 11, wherein the trending enginedetermines the velocity based on a number of clicks via one or moreencoded URL links of the encoded URL links within a predetermined timeperiod on content corresponding to a phrase.
 17. The system of claim 11,wherein the trending engine determines the velocity based on a rate ofclicks via one or more encoded URL links of the encoded URL links tocontent corresponding to a phrase.
 18. The system of claim 11, whereinthe trending engine determines the velocity based on a change in rate ofclicks via one or more encoded URL links of the encoded URL links tocontent corresponding to a phrase.
 19. The system of claim 11, whereinthe trending engine enumerates a list of phrases from the plurality ofphrases based on an increasing velocity of clicks.
 20. The system ofclaim 11, wherein the trending engine enumerates a list of phrases fromthe plurality of phrases based on a decreasing velocity of clicks.